Load frequency controllers considering renewable energy integration in power system
- Tungadio, Diambomba Hyacinthe, Sun, Yanxia
- Authors: Tungadio, Diambomba Hyacinthe , Sun, Yanxia
- Date: 2019
- Subjects: Load frequency control , Power system , Controllers
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/296605 , uj:32319 , Citation: Tungadio, D.H. & Sun, Y. 2019. Load frequency controllers considering renewable energy integration in power system.
- Description: Abstract: Load frequency control or automatic generation control is one of the main operations that take place daily in a modern power system. The objectives of load frequency control are to maintain power balance between interconnected areas and to control the power flow in the tie-lines. Electric power cannot be stored in large quantity that is why its production must be equal to the consumption in each time. This equation constitutes the key for a good management of any power system and introduces the need of more controllers when taking into account the integration of renewable energy sources into the traditional power system. There are many controllers presented in the literature and this work reviews the traditional load frequency controllers and those, which combined the traditional controller and artificial intelligence algorithms for controlling the load frequency.
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- Authors: Tungadio, Diambomba Hyacinthe , Sun, Yanxia
- Date: 2019
- Subjects: Load frequency control , Power system , Controllers
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/296605 , uj:32319 , Citation: Tungadio, D.H. & Sun, Y. 2019. Load frequency controllers considering renewable energy integration in power system.
- Description: Abstract: Load frequency control or automatic generation control is one of the main operations that take place daily in a modern power system. The objectives of load frequency control are to maintain power balance between interconnected areas and to control the power flow in the tie-lines. Electric power cannot be stored in large quantity that is why its production must be equal to the consumption in each time. This equation constitutes the key for a good management of any power system and introduces the need of more controllers when taking into account the integration of renewable energy sources into the traditional power system. There are many controllers presented in the literature and this work reviews the traditional load frequency controllers and those, which combined the traditional controller and artificial intelligence algorithms for controlling the load frequency.
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A new multi-swarm multi-objective particle swarm optimization based power and supply voltage unbalance optimization of three-phase submerged arc furnace
- Authors: Sun, Yanxia , Wang, Zenghui
- Date: 2015
- Subjects: Multi-objective optimization , Particle swarm optimization , Submerged arc furnace , Power optimization , Supply voltage unbalances
- Language: English
- Type: Conference proceedings
- Identifier: http://ujcontent.uj.ac.za8080/10210/390077 , http://hdl.handle.net/10210/20559 , uj:16112 , Citation: Sun, Y. & Wang, Z. 2015. A new multi-swarm multi-objective particle swarm optimization based power and supply voltage unbalance optimization of three-phase submerged arc furnace. Proceedings of the Sixth International Conference on Swarm Intelligence (ICSI 2015), Beijing, China, 25-29, June 2015.
- Description: Abstract: To improve the production ability of a three-phase submerged arc furnace (SAF), it is necessary to maximize the power input; minimize the supply voltage unbalances to reduce the side effect of the power grids. In this paper, maximizing the power input and minimum the supply voltage unbalances based on a proposed multi-swarm multi-objective particle swarm optimization algorithm are focused on. It is necessary to have objective functions when an optimization algorithm is applied. However, it is difficult to get the mathematic model of a three-phase submerged arc furnace according to its mechanisms because the system is complex and there are many disturbances. The neural networks (NN) have been applied since its ability can be used as an arbitrary function approximation mechanism based on the observed data. Based on the Pareto front, a multi-swarm multi-objective particle swarm optimization is pro-posed, which can be used to optimize the NN model of the three-phase SAF. The optimization results showed the efficiency of the proposed method.
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- Authors: Sun, Yanxia , Wang, Zenghui
- Date: 2015
- Subjects: Multi-objective optimization , Particle swarm optimization , Submerged arc furnace , Power optimization , Supply voltage unbalances
- Language: English
- Type: Conference proceedings
- Identifier: http://ujcontent.uj.ac.za8080/10210/390077 , http://hdl.handle.net/10210/20559 , uj:16112 , Citation: Sun, Y. & Wang, Z. 2015. A new multi-swarm multi-objective particle swarm optimization based power and supply voltage unbalance optimization of three-phase submerged arc furnace. Proceedings of the Sixth International Conference on Swarm Intelligence (ICSI 2015), Beijing, China, 25-29, June 2015.
- Description: Abstract: To improve the production ability of a three-phase submerged arc furnace (SAF), it is necessary to maximize the power input; minimize the supply voltage unbalances to reduce the side effect of the power grids. In this paper, maximizing the power input and minimum the supply voltage unbalances based on a proposed multi-swarm multi-objective particle swarm optimization algorithm are focused on. It is necessary to have objective functions when an optimization algorithm is applied. However, it is difficult to get the mathematic model of a three-phase submerged arc furnace according to its mechanisms because the system is complex and there are many disturbances. The neural networks (NN) have been applied since its ability can be used as an arbitrary function approximation mechanism based on the observed data. Based on the Pareto front, a multi-swarm multi-objective particle swarm optimization is pro-posed, which can be used to optimize the NN model of the three-phase SAF. The optimization results showed the efficiency of the proposed method.
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A novel approach for the identification of influential node in an electric power grid
- Adebayo, Isaiah G., Sun, Yanxia
- Authors: Adebayo, Isaiah G. , Sun, Yanxia
- Date: 2019
- Subjects: Influential node, Power network, Centrality measure
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/396397 , uj:32910 , Citation : Adebayo, I.G. 2019. A novel approach for the identification of influential node in an electric power grid @ Sun, Y.
- Description: Abstract : The continuous occurrence of blackouts due to cascading failures and voltage instability which has the effect of damaging several components within the grid has of recent been traced to sudden disconnection of influential node in a power network. How to identify this node has become a crucial issue to power system utilities. In this paper, an approach which is based on the network topological properties of an electric power system is investigated for the identification of influential node in a power network. First, we established a matrix which capture information between an electrical interconnection that exist between load to load nodes in a power network. Next, the suggested network structurally based eigenvector centrality measure (NSECM) is formulated based on the established matrix. A comparison analysis with a conventional eigenvector centrality measure is also performed to determine the significance of the proposed method. The effectiveness of all the approaches presented is tested on both practical 26-bus and the IEEE 14-bus power systems. Results obtained show that identification of influential node in a power network is better done using the NSECM method as it reduces the computational burden.
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- Authors: Adebayo, Isaiah G. , Sun, Yanxia
- Date: 2019
- Subjects: Influential node, Power network, Centrality measure
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/396397 , uj:32910 , Citation : Adebayo, I.G. 2019. A novel approach for the identification of influential node in an electric power grid @ Sun, Y.
- Description: Abstract : The continuous occurrence of blackouts due to cascading failures and voltage instability which has the effect of damaging several components within the grid has of recent been traced to sudden disconnection of influential node in a power network. How to identify this node has become a crucial issue to power system utilities. In this paper, an approach which is based on the network topological properties of an electric power system is investigated for the identification of influential node in a power network. First, we established a matrix which capture information between an electrical interconnection that exist between load to load nodes in a power network. Next, the suggested network structurally based eigenvector centrality measure (NSECM) is formulated based on the established matrix. A comparison analysis with a conventional eigenvector centrality measure is also performed to determine the significance of the proposed method. The effectiveness of all the approaches presented is tested on both practical 26-bus and the IEEE 14-bus power systems. Results obtained show that identification of influential node in a power network is better done using the NSECM method as it reduces the computational burden.
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Fuzzy logic system for intermixed biogas and photovoltaics measurement and control
- Matindife, Liston, Wang, Zenghui, Sun, Yanxia
- Authors: Matindife, Liston , Wang, Zenghui , Sun, Yanxia
- Date: 2018
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/274909 , uj:29351 , Citation: Matindife, L., Wang, Z. & Sun, Y. 2018. Fuzzy logic system for intermixed biogas and photovoltaics measurement and control. Hindawi Mathematical Problems in Engineering Volume 2018, Article ID 5412062, 18 pages https://doi.org/10.1155/2018/5412062.
- Description: Abstract: This study develops a new integrated measurement and control system for intermixed biogas and photovoltaic systems to achieve safe and optimal energy usage. Literature and field studies show that existing control methods on small- to medium-scale systems fall short of comprehensive system optimization and fault diagnosis, hence the need to revisit these control methods.The control strategy developed in this study is intelligent as it is wholly based on fuzzy logic algorithms. Fuzzy logic controllers due to their superior nonlinear problem solving capabilities to classical controllers considerably simplify controller design.The mathematical models that define classical controllers are difficult or impossible to realize in biogas and photovoltaic generation process. A microcontroller centered fuzzy logic measurement and control embedded system is designed and developed on the existing hybrid biogas and photovoltaic installations. The designed system is able to accurately predict digester stability, quantify biogas output, and carry out biogas fault detection and control. Optimized battery charging and photovoltaic fault detection and control are also successfully implemented. The system is able to optimize the operation and performance of biogas and photovoltaic energy generation.
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- Authors: Matindife, Liston , Wang, Zenghui , Sun, Yanxia
- Date: 2018
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/274909 , uj:29351 , Citation: Matindife, L., Wang, Z. & Sun, Y. 2018. Fuzzy logic system for intermixed biogas and photovoltaics measurement and control. Hindawi Mathematical Problems in Engineering Volume 2018, Article ID 5412062, 18 pages https://doi.org/10.1155/2018/5412062.
- Description: Abstract: This study develops a new integrated measurement and control system for intermixed biogas and photovoltaic systems to achieve safe and optimal energy usage. Literature and field studies show that existing control methods on small- to medium-scale systems fall short of comprehensive system optimization and fault diagnosis, hence the need to revisit these control methods.The control strategy developed in this study is intelligent as it is wholly based on fuzzy logic algorithms. Fuzzy logic controllers due to their superior nonlinear problem solving capabilities to classical controllers considerably simplify controller design.The mathematical models that define classical controllers are difficult or impossible to realize in biogas and photovoltaic generation process. A microcontroller centered fuzzy logic measurement and control embedded system is designed and developed on the existing hybrid biogas and photovoltaic installations. The designed system is able to accurately predict digester stability, quantify biogas output, and carry out biogas fault detection and control. Optimized battery charging and photovoltaic fault detection and control are also successfully implemented. The system is able to optimize the operation and performance of biogas and photovoltaic energy generation.
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Small world network based dynamic topology for particle swarm optimization
- Liu, Qingxue, Van Wyk, Barend Jacobus, Sun, Yanxia
- Authors: Liu, Qingxue , Van Wyk, Barend Jacobus , Sun, Yanxia
- Date: 2015
- Subjects: Particle swarm , Small world network , Neighborhood topology , Global model , Local model
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/22381 , uj:16196 , Citation: Liu, Q, Van Wyk, B.J. & Sun, Y. 2015. Small world network based dynamic topology for particle swarm optimization. 11th International Conference on Natural Computation (ICNC 2015). p. 289-294. ISBN: 978-1-4673-7678-5. DOI: 10.1109/ICNC.2015.7378005
- Description: Abstract: A new particle optimization algorithm with dynamic topology is proposed based on ‘small world’ network. The technique imitates the dissemination of information in a ‘small world network’ by dynamically updating the neighborhood topology of particle swarm optimization. The proposed dynamic neighborhood strategy can effectively coordinate the exploration and exploitation ability of particle swarm optimization. Simulations demonstrated that convergence of the swarms is guaranteed. Experiments demonstrated that the proposed method maintained the population diversity and enhanced the global search ability.
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- Authors: Liu, Qingxue , Van Wyk, Barend Jacobus , Sun, Yanxia
- Date: 2015
- Subjects: Particle swarm , Small world network , Neighborhood topology , Global model , Local model
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/22381 , uj:16196 , Citation: Liu, Q, Van Wyk, B.J. & Sun, Y. 2015. Small world network based dynamic topology for particle swarm optimization. 11th International Conference on Natural Computation (ICNC 2015). p. 289-294. ISBN: 978-1-4673-7678-5. DOI: 10.1109/ICNC.2015.7378005
- Description: Abstract: A new particle optimization algorithm with dynamic topology is proposed based on ‘small world’ network. The technique imitates the dissemination of information in a ‘small world network’ by dynamically updating the neighborhood topology of particle swarm optimization. The proposed dynamic neighborhood strategy can effectively coordinate the exploration and exploitation ability of particle swarm optimization. Simulations demonstrated that convergence of the swarms is guaranteed. Experiments demonstrated that the proposed method maintained the population diversity and enhanced the global search ability.
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Voltage stability based on a novel critical bus identification index
- Adebayo, Isaiah G., Sun, Yanxia
- Authors: Adebayo, Isaiah G. , Sun, Yanxia
- Date: 2019
- Subjects: Voltage stability , Voltage collapse , Power flow
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/394780 , uj:32705 , Citation: Adebayo, I.G. & Sun, Y. 2019. Voltage stability based on a novel critical bus identification index.
- Description: Abstract: The incessant occurrence of voltage collapse which shows in either partial or total blackout due to insufficient reactive power in a power system among other factors has of recent posed a great threat and concerned to power system utilities. This paper presents a voltage collapse critical bus index (VCCBI) method for the prediction of a weak bus in a power system. First, the voltage deviation of each load bus is computed at each reactive power load variation. Next, we computed for the VCCBI of each load bus by dividing the sum of the voltage deviation at each load bus by the product of the total sum of the step size and the maximum loadability of each load bus. Comparative analysis of the suggested VCCBI method is also done with the traditional voltage collapse proximity index (VCPI) and the L-Index. Result obtained shows that the approach based on VCCBI will be of great benefits as details information as may be needed by the power system utilities for the planning and operation of the system are obtained.
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- Authors: Adebayo, Isaiah G. , Sun, Yanxia
- Date: 2019
- Subjects: Voltage stability , Voltage collapse , Power flow
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/394780 , uj:32705 , Citation: Adebayo, I.G. & Sun, Y. 2019. Voltage stability based on a novel critical bus identification index.
- Description: Abstract: The incessant occurrence of voltage collapse which shows in either partial or total blackout due to insufficient reactive power in a power system among other factors has of recent posed a great threat and concerned to power system utilities. This paper presents a voltage collapse critical bus index (VCCBI) method for the prediction of a weak bus in a power system. First, the voltage deviation of each load bus is computed at each reactive power load variation. Next, we computed for the VCCBI of each load bus by dividing the sum of the voltage deviation at each load bus by the product of the total sum of the step size and the maximum loadability of each load bus. Comparative analysis of the suggested VCCBI method is also done with the traditional voltage collapse proximity index (VCPI) and the L-Index. Result obtained shows that the approach based on VCCBI will be of great benefits as details information as may be needed by the power system utilities for the planning and operation of the system are obtained.
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Fully connected multi-objective particle swarm optimizer based on neural network
- Authors: Wang, Zenghui , Sun, Yanxia
- Date: 2011
- Subjects: Multi-objective optimization , Particle swarm optimization , Neural network , Pareto front , Non domination
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/22087 , uj:16157 , Citation: Wang, Z. & Sun, Y. 2011. Fully connected multi-objective particle swarm optimizer based on neural network. Lecture notes in computer science 6838:170-177.
- Description: Abstract: In this paper, a new model for multi-objective particle swarm optimization (MOPSO) is proposed. In this model, each particle’s behavior is influenced by the best experience among its neighbors, its own best experience and all its components. The influence among different components of particles is implemented by the on-line training of a multi-input Multi-output back propagation (BP) neural network. The inputs and outputs of the BP neural network are the particle position and its the ’gradient descent’ direction vector to the less objective value according to the definition of no-domination, respectively. Therefore, the new structured MOPSO model is called a fully connected multi-objective particle swarm optimizer (FCMOPSO). Simulation results and comparisons with exiting MOPSOs demonstrate that the proposed FCMOPSO is more stable and can improve the optimization performance. , Originally presented at Fourth International Conference on Information and Computing (ICIC 2011), Phuket Island, Thailand 25 – 27 April 2011.
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- Authors: Wang, Zenghui , Sun, Yanxia
- Date: 2011
- Subjects: Multi-objective optimization , Particle swarm optimization , Neural network , Pareto front , Non domination
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/22087 , uj:16157 , Citation: Wang, Z. & Sun, Y. 2011. Fully connected multi-objective particle swarm optimizer based on neural network. Lecture notes in computer science 6838:170-177.
- Description: Abstract: In this paper, a new model for multi-objective particle swarm optimization (MOPSO) is proposed. In this model, each particle’s behavior is influenced by the best experience among its neighbors, its own best experience and all its components. The influence among different components of particles is implemented by the on-line training of a multi-input Multi-output back propagation (BP) neural network. The inputs and outputs of the BP neural network are the particle position and its the ’gradient descent’ direction vector to the less objective value according to the definition of no-domination, respectively. Therefore, the new structured MOPSO model is called a fully connected multi-objective particle swarm optimizer (FCMOPSO). Simulation results and comparisons with exiting MOPSOs demonstrate that the proposed FCMOPSO is more stable and can improve the optimization performance. , Originally presented at Fourth International Conference on Information and Computing (ICIC 2011), Phuket Island, Thailand 25 – 27 April 2011.
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Chaotic particle swarm optimization
- Sun, Yanxia, Qi, Guoyuan, Wang, Zenghui, Van Wyk, Barend Jacobus, Hamam, Yskandar
- Authors: Sun, Yanxia , Qi, Guoyuan , Wang, Zenghui , Van Wyk, Barend Jacobus , Hamam, Yskandar
- Date: 2009
- Subjects: Chaos , Particle swarm optimization , Neural network , Convergence
- Language: English
- Identifier: http://hdl.handle.net/10210/22298 , uj:16185 , Citation: Sun, Y. et al. 2009. Chaotic particle swarm optimization. 2009 World Summit on Genetic and Evolutionary Computation (2009 GEC Summit), Shanghai, China, June 12-14, 2009. p. 505-510. ISBN:16-05-58326-X.
- Description: Abstract: A new particle swarm optimization (PSO) algorithm with has a chaotic neural network structure, is proposed. The structure is similar to the Hop¯eld neural network with transient chaos, and has an improved ability to search for globally optimal solution and does not su®er from problems of premature convergence. The presented PSO model is discrete-time discrete-state. The bifurcation diagram of a particle shows that it converges to a stable fixed point from a strange attractor, guaranteeing system convergence.
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- Authors: Sun, Yanxia , Qi, Guoyuan , Wang, Zenghui , Van Wyk, Barend Jacobus , Hamam, Yskandar
- Date: 2009
- Subjects: Chaos , Particle swarm optimization , Neural network , Convergence
- Language: English
- Identifier: http://hdl.handle.net/10210/22298 , uj:16185 , Citation: Sun, Y. et al. 2009. Chaotic particle swarm optimization. 2009 World Summit on Genetic and Evolutionary Computation (2009 GEC Summit), Shanghai, China, June 12-14, 2009. p. 505-510. ISBN:16-05-58326-X.
- Description: Abstract: A new particle swarm optimization (PSO) algorithm with has a chaotic neural network structure, is proposed. The structure is similar to the Hop¯eld neural network with transient chaos, and has an improved ability to search for globally optimal solution and does not su®er from problems of premature convergence. The presented PSO model is discrete-time discrete-state. The bifurcation diagram of a particle shows that it converges to a stable fixed point from a strange attractor, guaranteeing system convergence.
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Adaptive optimal digital PID controller
- Authors: Sun, Yanxia , Wang, Zenghui
- Date: 2015
- Subjects: PID controller , Adaptive control , Parameter tuning , Particle swarm optimization
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/21202 , uj:16123 , Citation: Sun, Y. & Wang, Z. 2015. Adaptive optimal digital PID controller. Applied mechanics and materials 789-790:1021-1026.
- Description: Abstract: It is necessary to change the parameters of PID controller if the parameters of plants change or there are disturbances. Particle swarm optimization algorithm is a powerful optimization algorithm to find the global optimal values in the problem space. In this paper, the particle swarm optimization algorithm is used to identify the model of the plant and the parameter of digital PID controller online. The model of the plant is identified online according to the absolute error of the real system output and the identified model output. The digital PID parameters are tuned based on the identified model and they are adaptive if the model is changed. Simulations are done to validate the proposed method comparing with the classical PID controller. , Originally presented at 2014 International Conference on Mechatronics, Automation and Manufacturing (ICMAM 2014), Beijing, October 24-26, 2014.
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- Authors: Sun, Yanxia , Wang, Zenghui
- Date: 2015
- Subjects: PID controller , Adaptive control , Parameter tuning , Particle swarm optimization
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/21202 , uj:16123 , Citation: Sun, Y. & Wang, Z. 2015. Adaptive optimal digital PID controller. Applied mechanics and materials 789-790:1021-1026.
- Description: Abstract: It is necessary to change the parameters of PID controller if the parameters of plants change or there are disturbances. Particle swarm optimization algorithm is a powerful optimization algorithm to find the global optimal values in the problem space. In this paper, the particle swarm optimization algorithm is used to identify the model of the plant and the parameter of digital PID controller online. The model of the plant is identified online according to the absolute error of the real system output and the identified model output. The digital PID parameters are tuned based on the identified model and they are adaptive if the model is changed. Simulations are done to validate the proposed method comparing with the classical PID controller. , Originally presented at 2014 International Conference on Mechatronics, Automation and Manufacturing (ICMAM 2014), Beijing, October 24-26, 2014.
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A robust energy and reserve dispatch model for prosumer microgrids incorporating demand response aggregators
- Damisa, Uyikumhe, Nwulu, Nnamdi I., Sun, Yanxia
- Authors: Damisa, Uyikumhe , Nwulu, Nnamdi I. , Sun, Yanxia
- Date: 2018
- Subjects: Microgrid , Robust optimization , Operational dispatch
- Language: English
- Type: Article
- Identifier: http://ujcontent.uj.ac.za8080/10210/385963 , http://hdl.handle.net/10210/289857 , uj:31457 , Citation: Damisa, U., Nwulu, N.I. & Sun, Y. 2018. A robust energy and reserve dispatch model for prosumer microgrids incorporating demand response aggregators.
- Description: Abstract: The uncertainty introduced by intermittent renewable energy generation and prosumer energy imports makes operational planning of renewable energy‐assisted prosumer microgrids challenging. This is due to the difficulty in obtaining accurate forecasts of energy expected from these renewable energy sources and prosumers. Operators of such microgrids therefore require additional grid‐balancing tools to maintain power supply and demand balance during grid operation. In this paper, the impact of demand response aggregators (DRA’s) in a prosumer microgrid is investigated. This is achieved by developing and solving a deterministic mathematical formulation for the operational planning of the grid. Also, taking a cue from CAISO’s proposed tariff revision which allows the state‐of‐charge of non‐generator resources (like storage units) to be submitted as a bid parameter in the day‐ahead market and permits scheduling coordinators of these resources to self‐manage their energy limits and state‐of‐charge, the proposed formulation permits prosumers to submit battery energy content as a bid parameter and self‐manage their battery energy limits. Furthermore, a robust counterpart of the model is developed. Both formulations are constrained mixed integer optimization problems which are solved using the CPLEX solver in Advanced Interactive Multidimensional Modelling System (AIMMS) environment. Results obtained from tests carried out on a hypothetical prosumer microgrid show that the operating cost of the microgrid reduces in the presence of DRA’s. In addition, the storage facility owner may benefit from self‐managing its energy limits, but this may cut the amount of grid‐balancing resource available to the microgrid operator, thereby increasing the operating cost of the microgrid.
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- Authors: Damisa, Uyikumhe , Nwulu, Nnamdi I. , Sun, Yanxia
- Date: 2018
- Subjects: Microgrid , Robust optimization , Operational dispatch
- Language: English
- Type: Article
- Identifier: http://ujcontent.uj.ac.za8080/10210/385963 , http://hdl.handle.net/10210/289857 , uj:31457 , Citation: Damisa, U., Nwulu, N.I. & Sun, Y. 2018. A robust energy and reserve dispatch model for prosumer microgrids incorporating demand response aggregators.
- Description: Abstract: The uncertainty introduced by intermittent renewable energy generation and prosumer energy imports makes operational planning of renewable energy‐assisted prosumer microgrids challenging. This is due to the difficulty in obtaining accurate forecasts of energy expected from these renewable energy sources and prosumers. Operators of such microgrids therefore require additional grid‐balancing tools to maintain power supply and demand balance during grid operation. In this paper, the impact of demand response aggregators (DRA’s) in a prosumer microgrid is investigated. This is achieved by developing and solving a deterministic mathematical formulation for the operational planning of the grid. Also, taking a cue from CAISO’s proposed tariff revision which allows the state‐of‐charge of non‐generator resources (like storage units) to be submitted as a bid parameter in the day‐ahead market and permits scheduling coordinators of these resources to self‐manage their energy limits and state‐of‐charge, the proposed formulation permits prosumers to submit battery energy content as a bid parameter and self‐manage their battery energy limits. Furthermore, a robust counterpart of the model is developed. Both formulations are constrained mixed integer optimization problems which are solved using the CPLEX solver in Advanced Interactive Multidimensional Modelling System (AIMMS) environment. Results obtained from tests carried out on a hypothetical prosumer microgrid show that the operating cost of the microgrid reduces in the presence of DRA’s. In addition, the storage facility owner may benefit from self‐managing its energy limits, but this may cut the amount of grid‐balancing resource available to the microgrid operator, thereby increasing the operating cost of the microgrid.
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Improved genetic algorithm based on PSO-inspired reference point placement
- Essiet, Ima O., Sun, Yanxia, Wang, Zenghui
- Authors: Essiet, Ima O. , Sun, Yanxia , Wang, Zenghui
- Date: 2019
- Subjects: Non-dominated sorting genetic algorithm , Reference points , Optimization
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/395582 , uj:32807 , Citation: Essiet, I.O., Sun, Y. & Wang, Z. 2019. Improved genetic algorithm based on PSO-inspired reference point placement.
- Description: Abstract: This paper investigates the performance of the Non-dominated Sorting Genetic Algorithm (NSGA) based on the placement of reference points in the objective function space. An improved version of NSGA is proposed and its performance is analysed for five and eight reference points respectively in the multi-objective function space. The reference points are arranged as two effective swarm topologies: wheel and Von Neumann topology, which have been widely used in Particle Swarm Optimization (PSO). Through the simulations, the wheel topology (called wheel reference point genetic algorithm (wRPGA)) based method achieves better performance than the one which is based on the Von Neumann topology. The wheel topology also achieves better performance with respect to IGD compared to KnEA, NSGAIII and MOEAD/D for 7 out of 15 CEC 2017 benchmark problems. Moreover, wRPGA gives a good approximation of the Pareto front for the 3-objective model representing the hypothetical microgrid.
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- Authors: Essiet, Ima O. , Sun, Yanxia , Wang, Zenghui
- Date: 2019
- Subjects: Non-dominated sorting genetic algorithm , Reference points , Optimization
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/395582 , uj:32807 , Citation: Essiet, I.O., Sun, Y. & Wang, Z. 2019. Improved genetic algorithm based on PSO-inspired reference point placement.
- Description: Abstract: This paper investigates the performance of the Non-dominated Sorting Genetic Algorithm (NSGA) based on the placement of reference points in the objective function space. An improved version of NSGA is proposed and its performance is analysed for five and eight reference points respectively in the multi-objective function space. The reference points are arranged as two effective swarm topologies: wheel and Von Neumann topology, which have been widely used in Particle Swarm Optimization (PSO). Through the simulations, the wheel topology (called wheel reference point genetic algorithm (wRPGA)) based method achieves better performance than the one which is based on the Von Neumann topology. The wheel topology also achieves better performance with respect to IGD compared to KnEA, NSGAIII and MOEAD/D for 7 out of 15 CEC 2017 benchmark problems. Moreover, wRPGA gives a good approximation of the Pareto front for the 3-objective model representing the hypothetical microgrid.
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Prediction performance of improved decision tree based algorithms : a review
- Mienyea, Domor Ibomoiye, Sun, Yanxia, Wang, Zenghui
- Authors: Mienyea, Domor Ibomoiye , Sun, Yanxia , Wang, Zenghui
- Date: 2019
- Subjects: Machine learning, Data mining, Algorithm
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/397330 , uj:33022 , Mienyea, D.I. et al. 2019. Prediction performance of improved decision tree based algorithms : a review
- Description: Abstract : Applications of machine learning can be found in retail, banking, education, health sectors etc. To process the large data emanating from the various sectors, researchers are developing different algorithms using expertise from several fields and knowledge of existing algorithms. Machine learning decision tree algorithms which includes ID3, C4.5, C5.0, and CART (Classification and Regression Trees) are quite powerful. ID3 and C4.5 are mostly used in classification problems, and they are the focus of this research. C4.5 is an improved version of ID3 developed by Ross Quinlan. The prediction performance of these algorithms is very important. In this paper, the prediction performance of decision tree algorithms will be studied, an in-depth review will be conducted on relevant researches that attempted to improve the performance of the algorithms and the various methods used. Comparison will also be done between the various tree based algorithms. The major contribution of this review is to provide researchers with the progress made so far, as there is no available literature that has put together relevant improvements of decision tree based algorithms, and lastly lay the foundation for future research and improvements.
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- Authors: Mienyea, Domor Ibomoiye , Sun, Yanxia , Wang, Zenghui
- Date: 2019
- Subjects: Machine learning, Data mining, Algorithm
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/397330 , uj:33022 , Mienyea, D.I. et al. 2019. Prediction performance of improved decision tree based algorithms : a review
- Description: Abstract : Applications of machine learning can be found in retail, banking, education, health sectors etc. To process the large data emanating from the various sectors, researchers are developing different algorithms using expertise from several fields and knowledge of existing algorithms. Machine learning decision tree algorithms which includes ID3, C4.5, C5.0, and CART (Classification and Regression Trees) are quite powerful. ID3 and C4.5 are mostly used in classification problems, and they are the focus of this research. C4.5 is an improved version of ID3 developed by Ross Quinlan. The prediction performance of these algorithms is very important. In this paper, the prediction performance of decision tree algorithms will be studied, an in-depth review will be conducted on relevant researches that attempted to improve the performance of the algorithms and the various methods used. Comparison will also be done between the various tree based algorithms. The major contribution of this review is to provide researchers with the progress made so far, as there is no available literature that has put together relevant improvements of decision tree based algorithms, and lastly lay the foundation for future research and improvements.
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Analysis of the Qi three dimensional chaotic system
- Authors: Sun, Yanxia
- Date: 2009
- Subjects: Bifurcation , Hopf bifurcation , Center manifold theorem , Frequency spectral analysis , Chaos
- Type: Article
- Identifier: http://hdl.handle.net/10210/17913 , uj:15938 , Citation: Sun, Y. et al. 2009. Analysis of the Qi three dimensional chaotic system. Far east journal of dynamical systems, 11(1):77-94.
- Description: Abstract: This paper applies the center manifold theorem to reduce the dimensions of the Qi three-dimensional system. Local bifurcation phenomena are analyzed, including the pitchfork and Hopf bifurcations of the chaotic system. The Poincaré map is also investigated. The analyses demonstrate the rich dynamics of the Qi chaotic system. Finally, the frequency spectral analysis shows that the system has a broad frequency bandwidth, which is desirable for engineering applications such as secure communications.
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- Authors: Sun, Yanxia
- Date: 2009
- Subjects: Bifurcation , Hopf bifurcation , Center manifold theorem , Frequency spectral analysis , Chaos
- Type: Article
- Identifier: http://hdl.handle.net/10210/17913 , uj:15938 , Citation: Sun, Y. et al. 2009. Analysis of the Qi three dimensional chaotic system. Far east journal of dynamical systems, 11(1):77-94.
- Description: Abstract: This paper applies the center manifold theorem to reduce the dimensions of the Qi three-dimensional system. Local bifurcation phenomena are analyzed, including the pitchfork and Hopf bifurcations of the chaotic system. The Poincaré map is also investigated. The analyses demonstrate the rich dynamics of the Qi chaotic system. Finally, the frequency spectral analysis shows that the system has a broad frequency bandwidth, which is desirable for engineering applications such as secure communications.
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A new golden ratio local search based particle swarm optimization
- Sun, Yanxia, Van Wyk, Barend Jacobus, Wang, Zenghui
- Authors: Sun, Yanxia , Van Wyk, Barend Jacobus , Wang, Zenghui
- Date: 2012
- Subjects: Particle swarm optimization , Golden ratio , Local search
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/21329 , uj:16141 , Citation: Sun, Y., Van Wyk, B.J. & Wang, Z. 2012. A new golden ratio local search based particle swarm optimization. 2012 International Conference on Systems and Informatics (ICSAI 2012), Yantai, China 19-20 May, 2012. p. 754-757. ISBN: 978-1-4673-0198-5
- Description: Abstract: At beginning of the search process of particle swarm optimization, one of the disadvantages is that PSO focuses on the global search while the local search is weaken. However, at the end of the search procedure, the PSO focuses on the local search as all most all the particles converge to small areas which may make the particle swarm trapped in the local minima if no particle find position near the minima at the beginning of search procedure. To improve the optimization performance, the local search is necessary for particle swarm optimization. In this paper, golden ratio is used to determine the size of the search area. Only two positions need to be checked to find whether there are local positions with lower fitness value around a certain particle position. This method is easy to use. It is also tested using several famous benchmarks with high dimensions and big search space to the efficiency of the proposed method.
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- Authors: Sun, Yanxia , Van Wyk, Barend Jacobus , Wang, Zenghui
- Date: 2012
- Subjects: Particle swarm optimization , Golden ratio , Local search
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/21329 , uj:16141 , Citation: Sun, Y., Van Wyk, B.J. & Wang, Z. 2012. A new golden ratio local search based particle swarm optimization. 2012 International Conference on Systems and Informatics (ICSAI 2012), Yantai, China 19-20 May, 2012. p. 754-757. ISBN: 978-1-4673-0198-5
- Description: Abstract: At beginning of the search process of particle swarm optimization, one of the disadvantages is that PSO focuses on the global search while the local search is weaken. However, at the end of the search procedure, the PSO focuses on the local search as all most all the particles converge to small areas which may make the particle swarm trapped in the local minima if no particle find position near the minima at the beginning of search procedure. To improve the optimization performance, the local search is necessary for particle swarm optimization. In this paper, golden ratio is used to determine the size of the search area. Only two positions need to be checked to find whether there are local positions with lower fitness value around a certain particle position. This method is easy to use. It is also tested using several famous benchmarks with high dimensions and big search space to the efficiency of the proposed method.
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Optimization of PV systems using ANN-PSO configuration technique under different weather conditions
- Farayola, Adedayo M., Sun, Yanxia, Ali, Ahmed
- Authors: Farayola, Adedayo M. , Sun, Yanxia , Ali, Ahmed
- Date: 2018
- Subjects: MPPT , GMPPT , P&O
- Language: English
- Type: Conference proceeding
- Identifier: http://hdl.handle.net/10210/290080 , uj:31485 , Citation: Farayola, A.M., Sun, Y. & Ali, A. 2018. Optimization of PV systems using ANN-PSO configuration technique under different weather conditions.
- Description: Abstract: Conventional MPPT techniques like Perturb&observe perform ineffective under partial shading condition due to its inability to effectively track the global maximum power point (GMPP). Particle swarm optimization (PSO) technique is a recent meta-heuristic MPPT technique commonly used to extract maximum power from PV systems but takes time to iteratively locate the GMPP. This paper presents a novel use of hybrid ANNPSO technique implemented using series-connected distributive MPPT configuration approach. The results of ANN-PSO distributive MPPT, PSO, and Perturb&observe (P&O) technique were compared with theoretical power values under different weather conditions. This work was done to determine the most efficient MPPT method that can be considered for MPPT task in PV systems under uniform irradiance and partial shading conditions. Obtained results show that ANN-PSO DMPPT configuration exhibited the best performance.
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- Authors: Farayola, Adedayo M. , Sun, Yanxia , Ali, Ahmed
- Date: 2018
- Subjects: MPPT , GMPPT , P&O
- Language: English
- Type: Conference proceeding
- Identifier: http://hdl.handle.net/10210/290080 , uj:31485 , Citation: Farayola, A.M., Sun, Y. & Ali, A. 2018. Optimization of PV systems using ANN-PSO configuration technique under different weather conditions.
- Description: Abstract: Conventional MPPT techniques like Perturb&observe perform ineffective under partial shading condition due to its inability to effectively track the global maximum power point (GMPP). Particle swarm optimization (PSO) technique is a recent meta-heuristic MPPT technique commonly used to extract maximum power from PV systems but takes time to iteratively locate the GMPP. This paper presents a novel use of hybrid ANNPSO technique implemented using series-connected distributive MPPT configuration approach. The results of ANN-PSO distributive MPPT, PSO, and Perturb&observe (P&O) technique were compared with theoretical power values under different weather conditions. This work was done to determine the most efficient MPPT method that can be considered for MPPT task in PV systems under uniform irradiance and partial shading conditions. Obtained results show that ANN-PSO DMPPT configuration exhibited the best performance.
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Analysis of a fractional order nonlinear system based on the frequency domain approximation
- Authors: Wang, Zenghui , Sun, Yanxia
- Date: 2014
- Subjects: Nonlinear system , Fractional order system , Chaos , Hyperchaos
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/17800 , uj:15926 , Doi: 10.14355/ijepr.2014.0304.02 , Citation: Wang, Z., Sun, Y. 2014. Analysis of a fractional order nonlinear system based on the frequency domain approximation. International Journal of Engineering Practical Research (IJEPR), 3(4): 74-77.
- Description: Abstract: The dynamics of nonlinear system is very complicated especially the fractional nonlinear system since they can be found in many areas of engineering and science. The dynamics of the Lorenz system with fractional derivatives is analysed based on the frequency approximation. For a given range of parameters where the non‐fractional Lorenz system has periodic orbits, it is found that the fractional Lorenz system exhibits chaos and hyperchaos. A striking finding is that the fractional Lorenz system exhibits hyperchaos, although the total system order is less than 3, which is contrary to the well known conclusion that hyperchaos cannot occur in the integer‐order continuous‐time autonomous system of order less than 4. Finally, a reasonable explanation is offered for this complicated dynamical phenomenon.
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- Authors: Wang, Zenghui , Sun, Yanxia
- Date: 2014
- Subjects: Nonlinear system , Fractional order system , Chaos , Hyperchaos
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/17800 , uj:15926 , Doi: 10.14355/ijepr.2014.0304.02 , Citation: Wang, Z., Sun, Y. 2014. Analysis of a fractional order nonlinear system based on the frequency domain approximation. International Journal of Engineering Practical Research (IJEPR), 3(4): 74-77.
- Description: Abstract: The dynamics of nonlinear system is very complicated especially the fractional nonlinear system since they can be found in many areas of engineering and science. The dynamics of the Lorenz system with fractional derivatives is analysed based on the frequency approximation. For a given range of parameters where the non‐fractional Lorenz system has periodic orbits, it is found that the fractional Lorenz system exhibits chaos and hyperchaos. A striking finding is that the fractional Lorenz system exhibits hyperchaos, although the total system order is less than 3, which is contrary to the well known conclusion that hyperchaos cannot occur in the integer‐order continuous‐time autonomous system of order less than 4. Finally, a reasonable explanation is offered for this complicated dynamical phenomenon.
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Asynchronous and stochastic dimension updating PSO and its application to parameter estimation for frequency modulated (FM) sound waves
- Authors: Sun, Yanxia , Wang, Zenghui
- Date: 2016
- Subjects: Swarm optimization , Asynchronous updating , Stochastic dimension updating
- Language: English
- Type: Conference proceeding
- Identifier: http://hdl.handle.net/10210/92362 , uj:20221 , Citation: Sun, Y. & Wang, Z. 2016. Asynchronous and stochastic dimension updating PSO and its application to parameter estimation for frequency modulated (FM) sound waves.
- Description: Abstract: The particle velocity and position updating plays very important role for achieving good optimization performance for Particle Swarm Optimization (PSO). This paper analyzed the performance of asynchronously updating PSO and synchronously updating PSO by simulation and found that the asynchronously updating way can achieve better optimization performance than the synchronously updating way. Moreover, the convergence of asynchronously PSO is faster than the synchronously PSO, which means there is spare time to achieve better optimization performance based on some techniques. Here we proposed stochastic dimension updating technique which means only some dimensions of position will be updated. Several benchmark functions have been used to validate the proposed method and the proposed method is also applied to the parameter estimation for frequency modulated Sound Waves.
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- Authors: Sun, Yanxia , Wang, Zenghui
- Date: 2016
- Subjects: Swarm optimization , Asynchronous updating , Stochastic dimension updating
- Language: English
- Type: Conference proceeding
- Identifier: http://hdl.handle.net/10210/92362 , uj:20221 , Citation: Sun, Y. & Wang, Z. 2016. Asynchronous and stochastic dimension updating PSO and its application to parameter estimation for frequency modulated (FM) sound waves.
- Description: Abstract: The particle velocity and position updating plays very important role for achieving good optimization performance for Particle Swarm Optimization (PSO). This paper analyzed the performance of asynchronously updating PSO and synchronously updating PSO by simulation and found that the asynchronously updating way can achieve better optimization performance than the synchronously updating way. Moreover, the convergence of asynchronously PSO is faster than the synchronously PSO, which means there is spare time to achieve better optimization performance based on some techniques. Here we proposed stochastic dimension updating technique which means only some dimensions of position will be updated. Several benchmark functions have been used to validate the proposed method and the proposed method is also applied to the parameter estimation for frequency modulated Sound Waves.
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Local and global search based PSO algorithm
- Sun, Yanxia, Wang, Zenghui, Van Wyk, Barend Jacobus
- Authors: Sun, Yanxia , Wang, Zenghui , Van Wyk, Barend Jacobus
- Date: 2013
- Subjects: Local search , Global search , Particle swarm optimisation
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/21240 , uj:16129 , Citation: Sun,Y., Wang, Z. & Van Wyk, B. 2013. Local and global search based PSO algorithm. Lecture Notes in Computer Sciences (LNCS) 7928: 129-136. ISSN: 0302-9743
- Description: Abstract: In this paper, a new algorithm for particle swarm optimisation (PSO) is proposed. In this algorithm, the particles are divided into two groups. The two groups have different focuses when all the particles are searching the problem space. The first group of particles will search the area around the best experience of their neighbours. The particles in the second group are influenced by the best experience of their neighbors and the individual best experience, which is the same as the standard PSO. Simulation results and comparisons with the standard PSO 2007 demonstrate that the proposed algorithm effectively enhances searching efficiency and improves the quality of searching. , Originally presented at Fourth International Conference on Swarm Intelligence (ICSI 2013), Harbin, China, 12-15, June, 2013.
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- Authors: Sun, Yanxia , Wang, Zenghui , Van Wyk, Barend Jacobus
- Date: 2013
- Subjects: Local search , Global search , Particle swarm optimisation
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/21240 , uj:16129 , Citation: Sun,Y., Wang, Z. & Van Wyk, B. 2013. Local and global search based PSO algorithm. Lecture Notes in Computer Sciences (LNCS) 7928: 129-136. ISSN: 0302-9743
- Description: Abstract: In this paper, a new algorithm for particle swarm optimisation (PSO) is proposed. In this algorithm, the particles are divided into two groups. The two groups have different focuses when all the particles are searching the problem space. The first group of particles will search the area around the best experience of their neighbours. The particles in the second group are influenced by the best experience of their neighbors and the individual best experience, which is the same as the standard PSO. Simulation results and comparisons with the standard PSO 2007 demonstrate that the proposed algorithm effectively enhances searching efficiency and improves the quality of searching. , Originally presented at Fourth International Conference on Swarm Intelligence (ICSI 2013), Harbin, China, 12-15, June, 2013.
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Optimal power control strategy of a distributed energy system incorporating demand response
- Authors: Dzobo, Oliver , Sun, Yanxia
- Date: 2016
- Subjects: Smart home appliance , Distributed energy system , Total daily electricity cost
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/216834 , uj:21558 , Citation: Dzobo, O. & Sun, Y. 2016. Optimal power control strategy of a distributed energy system incorporating demand response.
- Description: Abstract: This paper presents an optimal power control scheduling of a distributed energy system in presence of demand response. The distributed energy system comprises of a solar photovoltaic (PV) module and a battery bank storage system. A non-convex mixed binary integer programming technique is used to model flexible and inflexible smart home appliances. Two scenarios are considered in the case study. The results show that efficient scheduling of smart home appliances combined with optimal control of distributed energy system can significantly reduce the total daily electricity cost by more than 50%. The optimal control of distributed energy system was also shown to have an effect on the scheduling of smart home appliances.
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- Authors: Dzobo, Oliver , Sun, Yanxia
- Date: 2016
- Subjects: Smart home appliance , Distributed energy system , Total daily electricity cost
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/216834 , uj:21558 , Citation: Dzobo, O. & Sun, Y. 2016. Optimal power control strategy of a distributed energy system incorporating demand response.
- Description: Abstract: This paper presents an optimal power control scheduling of a distributed energy system in presence of demand response. The distributed energy system comprises of a solar photovoltaic (PV) module and a battery bank storage system. A non-convex mixed binary integer programming technique is used to model flexible and inflexible smart home appliances. Two scenarios are considered in the case study. The results show that efficient scheduling of smart home appliances combined with optimal control of distributed energy system can significantly reduce the total daily electricity cost by more than 50%. The optimal control of distributed energy system was also shown to have an effect on the scheduling of smart home appliances.
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Optimized energy consumption model for smart home using improved differential evolution algorithm
- Essiet, Ima O., Sun, Yanxia, Wang, Zenghui
- Authors: Essiet, Ima O. , Sun, Yanxia , Wang, Zenghui
- Date: 2019
- Subjects: HEMS , Evolutionary algorithms , RES
- Language: English
- Type: Article
- Identifier: http://ujcontent.uj.ac.za8080/10210/383615 , http://hdl.handle.net/10210/291789 , uj:31698 , Citation: Essiet, I.O, Sun, Y. & Wang, Z. 2019. Optimized energy consumption model for smart home using improved differential evolution algorithm.
- Description: Abstract: This paper proposes an improved enhanced differential evolution algorithm for implementing demand response between aggregator and consumer. The proposed algorithm utilizes a secondary population archive, which contains unfit solutions that are discarded by the primary archive of the earlier proposed enhanced differential evolution algorithm. The secondary archive initializes, mutates and recombines candidates in order to improve their fitness and then passes them back to the primary archive for possible selection. The capability of this proposed algorithm is confirmed by comparing its performance with three other wellperforming evolutionary algorithms: enhanced differential evolution, multiobjective evolutionary algorithm based on dominance and decomposition, and non-dominated sorting genetic algorithm III. This is achieved by testing the algorithms’ ability to optimize a multiobjective optimization problem representing a smart home with demand response aggregator. Shiftable and non-shiftable loads are considered for the smart home which model energy usage profile for a typical household in Johannesburg, South Africa. In this study, renewable sources include battery bank and rooftop photovoltaic panels. Simulation results show that the proposed algorithm is able to optimize energy usage by balancing load scheduling and contribution of renewable sources, while maximizing user comfort and minimizing peak-to-average ratio.
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- Authors: Essiet, Ima O. , Sun, Yanxia , Wang, Zenghui
- Date: 2019
- Subjects: HEMS , Evolutionary algorithms , RES
- Language: English
- Type: Article
- Identifier: http://ujcontent.uj.ac.za8080/10210/383615 , http://hdl.handle.net/10210/291789 , uj:31698 , Citation: Essiet, I.O, Sun, Y. & Wang, Z. 2019. Optimized energy consumption model for smart home using improved differential evolution algorithm.
- Description: Abstract: This paper proposes an improved enhanced differential evolution algorithm for implementing demand response between aggregator and consumer. The proposed algorithm utilizes a secondary population archive, which contains unfit solutions that are discarded by the primary archive of the earlier proposed enhanced differential evolution algorithm. The secondary archive initializes, mutates and recombines candidates in order to improve their fitness and then passes them back to the primary archive for possible selection. The capability of this proposed algorithm is confirmed by comparing its performance with three other wellperforming evolutionary algorithms: enhanced differential evolution, multiobjective evolutionary algorithm based on dominance and decomposition, and non-dominated sorting genetic algorithm III. This is achieved by testing the algorithms’ ability to optimize a multiobjective optimization problem representing a smart home with demand response aggregator. Shiftable and non-shiftable loads are considered for the smart home which model energy usage profile for a typical household in Johannesburg, South Africa. In this study, renewable sources include battery bank and rooftop photovoltaic panels. Simulation results show that the proposed algorithm is able to optimize energy usage by balancing load scheduling and contribution of renewable sources, while maximizing user comfort and minimizing peak-to-average ratio.
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