Considering the economics and securities for the operation of a power system, this paper presents a new adaptive dynamic programming approach for security-constrained unit commitment (SCUC) problems. In response to t...Considering the economics and securities for the operation of a power system, this paper presents a new adaptive dynamic programming approach for security-constrained unit commitment (SCUC) problems. In response to the “curse of dimension” problem of dynamic programming, the approach solves the Bellman’s equation of SCUC approximately by solving a sequence of simplified single stage optimization problems. An extended sequential truncation technique is proposed to explore the state space of the approach, which is superior to traditional sequential truncation in daily cost for unit commitment. Different test cases from 30 to 300 buses over a 24 h horizon are analyzed. Extensive numerical comparisons show that the proposed approach is capable of obtaining the optimal unit commitment schedules without any network and bus voltage violations, and minimizing the operation cost as well.展开更多
This paper presents a novel approach to solve the Multi-Area unit commitment problem using particle swarm optimization technique. The objective of the multi-area unit commitment problem is to determine the optimal or ...This paper presents a novel approach to solve the Multi-Area unit commitment problem using particle swarm optimization technique. The objective of the multi-area unit commitment problem is to determine the optimal or a near optimal commitment strategy for generating the units. And it is located in multiple areas that are interconnected via tie lines and joint operation of generation resources can result in significant operational cost savings. The dynamic programming method is applied to solve Multi-Area Unit Commitment problem and particle swarm optimization technique is embedded for computing the generation assigned to each area and the power allocated to all committed unit. Particle Swarm Optimization technique is developed to derive its Pareto-optimal solutions. The tie-line transfer limits are considered as a set of constraints during the optimization process to ensure the system security and reliability. Case study of four areas each containing 26 units connected via tie lines has been taken for analysis. Numerical results are shown comparing the cost solutions and computation time obtained by using the Particle Swarm Optimization method is efficient than the conventional Dynamic Programming and Evolutionary Programming Method.展开更多
Many studies have considered the solution of Unit Commitment problems for the management of energy networks. In this field, earlier work addressed the problem in determinist cases and in cases dealing with demand unce...Many studies have considered the solution of Unit Commitment problems for the management of energy networks. In this field, earlier work addressed the problem in determinist cases and in cases dealing with demand uncertainties. In this paper, the authors develop a method to deal with uncertainties related to the cost function. Indeed, such uncertainties often occur in energy networks （waste incinerator with a priori unknown waste amounts, cogeneration plant with uncertainty of the sold electricity price...）. The corresponding optimization problems are large scale stochastic non-linear mixed integer problems. The developed solution method is a recourse based programming one. The main idea is to consider that amounts of energy to produce can be slightly adapted in real time, whereas the on/off statuses of units have to be decided very early in the management procedure. Results show that the proposed approach remains compatible with existing Unit Commitment programming methods and presents an obvious interest with reasonable computing loads.展开更多
In the present electricity market, where renewable energy power plants have been included in the power systems, there is a lot of unpredictability in the demand and generation. There are many conventional and evolutio...In the present electricity market, where renewable energy power plants have been included in the power systems, there is a lot of unpredictability in the demand and generation. There are many conventional and evolutionary programming techniques used for solving the unit commitment （UC） problem. Dynamic programming （DP） is a conventional algorithm used to solve the deterministic problem. In this paper DP is used to solve the stochastic model of UC problem. The stochastic modeling for load and generation side has been formulated using an approximate state decision approach. The programs were developed in a MATLAB environment and were exten- sively tested for a four-unit eight-hour system. The results obtained from these techniques were validated with the available literature and outcome was good. The commitment is in such a way that the total cost is minimal. The novelty of this paper lies in the fact that DP is used for solving the stochastic UC problem.展开更多
During this decade,many countries have experienced natural and accidental disasters,such as typhoons,floods,earthquakes,and nuclear plant accidents,causing catastrophic damage to infrastructures.Since the end of 2019,...During this decade,many countries have experienced natural and accidental disasters,such as typhoons,floods,earthquakes,and nuclear plant accidents,causing catastrophic damage to infrastructures.Since the end of 2019,all countries of the world are struggling with the COVID-19 and pursuing countermeasures,including inoculation of vaccine,and changes in our lifestyle and social structures.All these experiences have made the residents in the affected regions keenly aware of the need for new infrastructures that are resilient and autonomous,so that vital lifelines are secured during calamities.A paradigm shift has been taking place toward reorganizing the energy social service management in many countries,including Japan,by effective use of sustainable energy and new supply schemes.However,such new power sources and supply schemes would affect the power grid through intermittency of power output and the deterioration of power quality and service.Therefore,new social infrastructures and novel management systems to supply energy and social service will be required.In this paper,user-friendly design,operation and control assist tools for resilient microgrids and autonomous communities are proposed and applied to the standard microgrid to verify its effectiveness and performance.展开更多
文摘Considering the economics and securities for the operation of a power system, this paper presents a new adaptive dynamic programming approach for security-constrained unit commitment (SCUC) problems. In response to the “curse of dimension” problem of dynamic programming, the approach solves the Bellman’s equation of SCUC approximately by solving a sequence of simplified single stage optimization problems. An extended sequential truncation technique is proposed to explore the state space of the approach, which is superior to traditional sequential truncation in daily cost for unit commitment. Different test cases from 30 to 300 buses over a 24 h horizon are analyzed. Extensive numerical comparisons show that the proposed approach is capable of obtaining the optimal unit commitment schedules without any network and bus voltage violations, and minimizing the operation cost as well.
文摘This paper presents a novel approach to solve the Multi-Area unit commitment problem using particle swarm optimization technique. The objective of the multi-area unit commitment problem is to determine the optimal or a near optimal commitment strategy for generating the units. And it is located in multiple areas that are interconnected via tie lines and joint operation of generation resources can result in significant operational cost savings. The dynamic programming method is applied to solve Multi-Area Unit Commitment problem and particle swarm optimization technique is embedded for computing the generation assigned to each area and the power allocated to all committed unit. Particle Swarm Optimization technique is developed to derive its Pareto-optimal solutions. The tie-line transfer limits are considered as a set of constraints during the optimization process to ensure the system security and reliability. Case study of four areas each containing 26 units connected via tie lines has been taken for analysis. Numerical results are shown comparing the cost solutions and computation time obtained by using the Particle Swarm Optimization method is efficient than the conventional Dynamic Programming and Evolutionary Programming Method.
文摘Many studies have considered the solution of Unit Commitment problems for the management of energy networks. In this field, earlier work addressed the problem in determinist cases and in cases dealing with demand uncertainties. In this paper, the authors develop a method to deal with uncertainties related to the cost function. Indeed, such uncertainties often occur in energy networks （waste incinerator with a priori unknown waste amounts, cogeneration plant with uncertainty of the sold electricity price...）. The corresponding optimization problems are large scale stochastic non-linear mixed integer problems. The developed solution method is a recourse based programming one. The main idea is to consider that amounts of energy to produce can be slightly adapted in real time, whereas the on/off statuses of units have to be decided very early in the management procedure. Results show that the proposed approach remains compatible with existing Unit Commitment programming methods and presents an obvious interest with reasonable computing loads.
文摘In the present electricity market, where renewable energy power plants have been included in the power systems, there is a lot of unpredictability in the demand and generation. There are many conventional and evolutionary programming techniques used for solving the unit commitment （UC） problem. Dynamic programming （DP） is a conventional algorithm used to solve the deterministic problem. In this paper DP is used to solve the stochastic model of UC problem. The stochastic modeling for load and generation side has been formulated using an approximate state decision approach. The programs were developed in a MATLAB environment and were exten- sively tested for a four-unit eight-hour system. The results obtained from these techniques were validated with the available literature and outcome was good. The commitment is in such a way that the total cost is minimal. The novelty of this paper lies in the fact that DP is used for solving the stochastic UC problem.
文摘During this decade,many countries have experienced natural and accidental disasters,such as typhoons,floods,earthquakes,and nuclear plant accidents,causing catastrophic damage to infrastructures.Since the end of 2019,all countries of the world are struggling with the COVID-19 and pursuing countermeasures,including inoculation of vaccine,and changes in our lifestyle and social structures.All these experiences have made the residents in the affected regions keenly aware of the need for new infrastructures that are resilient and autonomous,so that vital lifelines are secured during calamities.A paradigm shift has been taking place toward reorganizing the energy social service management in many countries,including Japan,by effective use of sustainable energy and new supply schemes.However,such new power sources and supply schemes would affect the power grid through intermittency of power output and the deterioration of power quality and service.Therefore,new social infrastructures and novel management systems to supply energy and social service will be required.In this paper,user-friendly design,operation and control assist tools for resilient microgrids and autonomous communities are proposed and applied to the standard microgrid to verify its effectiveness and performance.
