Economic Power Dispatching from Distributed Generations: Review of Optimization Techniques

In the increasingly decentralized energy environment,economical power dispatching from distributed generations(DGs)is crucial to minimizing operating costs,optimizing resource utilization,and guaranteeing a consistent and sustainable supply of electricity.A comprehensive review of optimization techniques for economic power dispatching from distributed generations is imperative to identify the most effective strategies for minimizing operational costs while maintaining grid stability and sustainability.The choice of optimization technique for economic power dispatching from DGs depends on a number of factors,such as the size and complexity of the power system,the availability of computational resources,and the specific requirements of the application.Optimization techniques for economic power dispatching from distributed generations(DGs)can be classified into two main categories:(i)Classical optimization techniques,(ii)Heuristic optimization techniques.In classical optimization techniques,the linear programming(LP)model is one of the most popular optimization methods.Utilizing the LP model,power demand and network constraints are met while minimizing the overall cost of generating electricity from DGs.This approach is efficient in determining the best DGs dispatch and is capable of handling challenging optimization issues in the large-scale system including renewables.The quadratic programming(QP)model,a classical optimization technique,is a further popular optimization method,to consider non-linearity.The QP model can take into account the quadratic cost of energy production,with consideration constraints like network capacity,voltage,and frequency.The metaheuristic optimization techniques are also used for economic power dispatching from DGs,which include genetic algorithms(GA),particle swarm optimization(PSO),and ant colony optimization(ACO).Also,Some researchers are developing hybrid optimization techniques that combine elements of classical and heuristic optimization techniques with the incorporation of droop control,predictive control,and fuzzy-based methods.These methods can deal with large-scale systems with many objectives and non-linear,non-convex optimization issues.The most popular approaches are the LP and QP models,while more difficult problems are handled using metaheuristic optimization techniques.In summary,in order to increase efficiency,reduce costs,and ensure a consistent supply of electricity,optimization techniques are essential tools used in economic power dispatching from DGs.

Two-Stage Optimal Dispatching of Wind Power-Photovoltaic-Solar Thermal Combined System Considering Economic Optimality and Fairness

Aiming at the problems of large-scale wind and solar grid connection,how to ensure the economy of system operation and how to realize fair scheduling between new energy power stations,a two-stage optimal dispatching model of wind power-photovoltaic-solar thermal combined system considering economic optimality and fairness is proposed.Firstly,the first stage dispatching model takes the overall economy optimization of the system as the goal and the principle of maximizing the consumption of wind and solar output,obtains the optimal output value under the economic conditions of each new energy station,and then obtains the maximum consumption space of the new energy station.Secondly,based on the optimization results of the first stage,the second stage dispatching model uses the dispatching method of fuzzy comprehensive ranking priority to prioritize the new energy stations,and then makes a fair allocation to the dispatching of the wind and solar stations.Finally,the analysis of a specific example shows that themodel can take into account the fairness of active power distribution of new energy stations on the basis of ensuring the economy of system operation,make full use of the consumption space,and realize the medium and long-term fairness distribution of dispatching plan.

Multi-timescale robust dispatching for coordinated automatic generation control and energy storage

The increasing penetration of renewable energy into power grids is reducing the regulation capacity of automatic generation control(AGC).Thus,there is an urgent demand to coordinate AGC units with active equipment such as energy storage.Current dispatch decision-making methods often ignore the intermittent effects of renewable energy.This paper proposes a two-stage robust optimization model in which energy storage is used to compensate for the intermittency of renewable energy for the dispatch of AGC units.This model exploits the rapid adjustment capability of energy storage to compensate for the slow response speed of AGC units,improve the adjustment potential,and respond to the problems of intermittent power generation from renewable energy.A column and constraint generation algorithm is used to solve the model.In an example analysis,the proposed model was more robust than a model that did not consider energy storage at eliminating the effects of intermittency while offering clear improvements in economy and efficiency.

Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control

With integration of large-scale renewable energy,new controllable devices,and required reinforcement of power grids,modern power systems have typical characteristics such as uncertainty,vulnerability and openness,which makes operation and control of power grids face severe security challenges.Application of artificial intelligence(AI)technologies represented by machine learning in power grid regulation is limited by reliability,interpretability and generalization ability of complex modeling.Mode of hybrid-augmented intelligence(HAI)based on human-machine collaboration(HMC)is a pivotal direction for future development of AI technology in this field.Based on characteristics of applications in power grid regulation,this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence(HHI)system for large-scale power grid dispatching and control(PGDC).First,theory and application scenarios of HHI are introduced and analyzed;then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed.Key technologies are discussed to achieve a thorough integration of human/machine intelligence.Finally,state-of-theart and future development of HHI in power grid regulation are summarized,aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.

Advanced in power control for distributed generation system using cooperative algorithms

To develop efficient power control strategies for a distributed generation system in order to improve the overall system efficiency, we propose a cooperative algorithm to analyze and design the controller, in which elements of conventional mathematical optimization algorithms are combined with adaptive dynamic elements drawn from intelligent control theory. In our design, the sequential quadratic programming algorithm was first utilized to obtain an optimal solution for power distribution among multiple units. Fuzzy system was then developed to implement the optimal strategies on the basis of optimal solution. In addition, parameters of the fuzzy system were adapted via a genetic algorithm. Tbe simulation results illustrate that the methodology described is useful for a range of control system designs.

Risk-averse Robust Interval Economic Dispatch for Power Systems with Large-scale Wind Power Integration

This paper presents a robust interval economic dispatch(RIED)model for power systems with large-scale wind power integration.Differing from existing interval optimization(IO)approaches that merely rely on the upper and lower boundaries of random variables,the distribution information retained in the historical data is introduced to the IO method in this paper.Based on the available probability distribution function(PDF),wind power curtailment and load shedding are quantified as the operational risk and incorporated into the decision-making process.In this model,we need not rely on the forecasted value of wind power,which is randomly fluctuating and quite unpredictable.Furthermore,when the PDFs of wind power are taken into account,the resulting dispatch solution makes a good tradeoff between the generation cost and the operational risk.Finally,the RIED model yields an optimal dispatch solution for thermal units and the allowable intervals of wind power for the wind farms,which efficiently mitigates the uncertainty in wind power generation and provides more practical suggestions for system operators.Simulation studies are conducted on a modified IEEE-118 bus system and the results verify the effectiveness of the proposed RIED model.

Weighing the Pros and Cons: Transformation of Angle of View for Three Gorges Dam

Three Gorges Dam Project is the world’s largest water-control and hydraulic-electric project in the 21st century. However, it has been shrouded in controversy since long even before it began construction in 1994. On one hand, the benefits of the Three Gorges Dam are flood control, power generation, navigation, aquaculture, tourism, ecological protection, environmental purification, development-oriented resettlement, transfer of water from southern China to northern China, and water supply and irrigation. On the other hand, the drawbacks of the dam are environmental impact, effect on local culture and aesthetic values, and sedimentation. In an environmental impact point of view, the Three Gorges Dam shows evidence that it has more advantages than disadvantages. Thus, by carefully balancing the pros and cons, one can say that the Three Gorges Dam is an environmentally feasible project.

Grey Wolf Optimizer to Real Power Dispatch with Non-Linear Constraints

A new and efficient Grey Wolf Optimization(GWO)algorithm is implemented to solve real power economic dispatch(RPED)problems in this paper.The nonlinear RPED problem is one the most important and fundamental optimization problem which reduces the total cost in generating real power without violating the constraints.Conventional methods can solve the ELD problem with good solution quality with assumptions assigned to fuel cost curves without which these methods lead to suboptimal or infeasible solutions.The behavior of grey wolves which is mimicked in the GWO algorithm are leadership hierarchy and hunting mechanism.The leadership hierarchy is simulated using four types of grey wolves.In addition,searching,encircling and attacking of prey are the social behaviors implemented in the hunting mechanism.The GWO algorithm has been applied to solve convex RPED problems considering the all possible constraints.The results obtained from GWO algorithm are compared with other state-ofthe-art algorithms available in the recent literatures.It is found that the GWO algorithm is able to provide better solution quality in terms of cost,convergence and robustness for the considered ELD problems.

Research on Wind Farm Participation in Power Grid AGC Control

Since wind power has the features of being intermittent and unpredictable, and usually needs transmission over long distances, grid integration of large-scale wind power will exert signif icant influence on power grid planning and construction, and will make a heavy impact on the safe and reliable operation of power systems. To deal with the diff iculties of large scale wind power dispatch, this paper presents a new automatic generation control (AGC) scheme that involves the participation of wind farms. The scheme is based on ultra-short-term wind power forecast. The author establishes a generation output distribution optimization mode for the power system with wind farms and verif ies the feasibility of the scheme by an example.

Multicriteria Methods for Distributed Generation Resources Optimization

The optimization process of embedded, or DG （distributed generation） is a very complex task, and it should be evaluated and compared by means of multi-criteria methods of analysis. The classical method for selection is usually based only on a single criterion analysis, and it is defined by thermal or economic aspects. The problem of optimal dispatch of DG is typical example of optimization, because it differs from the classical problem of generation dispatch in the power system, due to the specific criteria related to the DG interconnection. The most important goals are to maximize the renewable production and to minimize the total cost, while satisfying additional constraints related to the operation of a distribution network. As there are many DGs in a distribution network, it is very complicated to decide the optimal DG outputs to satisfy all the criteria and constraints imposed by the distribution network. Another problem is the lack of the dispatcher control over DGs, and very often, the only available action is to switch on or off the generator. Finally, network operator and DG owner perspective are often opposed regarding appropriate control action in the network. In this paper, a multicriteria decision support based on AHP （analytical hierarchical processes） method is proposed for the choice of the dispatching action. The method is illustrated on the choice of the DG to be switched off in the case or reverse power flow.

Integrated approach for optimal island partition and power dispatch

Operating in island mode when failure occurs in sure to maintain an uninterrupted power supply to significant loads. A two-stage approach that integrates optimal island partition and power dispatch is proposed in this paper, considering photovoltaics(PVs), batteries(BEs) and electric vehicles(EVs) as the power sources. In the first stage, energy indices are defined to describe the energy demand and the maximum energy that these distributed energy resources(DERs) can provide, and islands are partitioned based on an energy constraint. Considering the variability the loads and PVs, the energy constraint is a necessary but not sufficient condition for island operation,so in the second stage, a power dispatch model is proposed the active distribution network(ADN) is an effective mea-as a test for the island partition result. Sequential power flow is also simulated to guarantee a feasible and optimized island status. The situations when the tests are not passed are analyzed and classified, and corresponding modifications for the first stage model are provided. Multiple levels of constraints based on the energy index are established for the island partition model. The proposed approach has been validated through simulation using a modified IEEE 69-bus system which is divided into three districts with different load variability characteristics.

Optimal Active Power Dispatching of Microgrid and Distribution Network Based on Model Predictive Control

First, a three-tier coordinated scheduling system consisting of a distribution network dispatch layer, a microgrid centralized control layer, and local control layer in the energy internet is proposed. The multi-time scale optimal scheduling of the microgrid based on Model Predictive Control（MPC） is then studied, and the optimized genetic algorithm and the microgrid multi-time rolling optimization strategy are used to optimize the datahead scheduling phase and the intra-day optimization phase. Next, based on the three-tier coordinated scheduling architecture, the operation loss model of the distribution network is solved using the improved branch current forward-generation method and the genetic algorithm. The optimal scheduling of the distribution network layer is then completed. Finally, the simulation examples are used to compare and verify the validity of the method.

Optimized Controller Gains Using Grey Wolf Algorithm for Grid Tied Solar Power Generation with Improved Dynamics and Power Quality

This study proposes a control algorithm based on synchronous reference frame theory with unit templates instead of a phase locked loop for grid-connected photovoltaic(PV)solar system,comprising solar PV panels,DC-DC converter,controller for maximum power point tracking,resistance capacitance ripple filter,insulated-gate bipolar transistor based controller,interfacing inductor,linear and nonlinear loads.The dynamic performance of the grid connected solar system depends on the effect operation of the control algorithm,comprising two proportional-integral controllers.These controllers estimate the reference solar-grid currents,which in turn generate pulses for the three-leg voltage source converter.The grey wolf optimization algorithm is used to optimize the controller gains of the proportional-integral controllers,resulting in excellent performance compared to that of existing optimization algorithms.The compensation for neutral current is provided by a star-delta transformer(non-isolated),and the proposed solar PV grid system provides zero voltage regulation and eliminates harmonics,in addition to load balancing.Maximum power extraction from the solar panel is achieved using the incremental conductance algorithm for the DC-DC converter supplying solar power to the DC bus capacitor,which in turn supplies this power to the grid with improved dynamics and quality.The solar system along with the control algorithm and controller is modeled using Simulink in Matlab 2019.

考虑梅雨汛期防洪安全的水电富集型虚拟电厂优化调度

梅雨季节是中国长江中下游地区的特殊汛期,持续性降雨和突发性暴雨特性导致水电站群的调度策略尤其要兼顾流域防洪安全。为此,提出了考虑梅雨汛期防洪风险的水电富集型虚拟电厂(HEVPP)调度策略。首先,基于典型梅雨时空分布以及水电站防洪安全裕度特性,设计了考虑防洪风险偏好及降雨量不确定性的梅雨汛期库容调控方法;其次,针对HEVPP进行防洪风险的精细化建模,定义了基于剩余防洪库容理论的HEVPP防洪性能评估指标,并进一步提出了基于安全裕度的条件风险价值(CVaR)防洪风险成本计算方法;然后,以虚拟电厂经济效益最大化为目标,构建了考虑梅雨季节降雨特性及防洪风险成本的HEVPP优化调度模型;最后,通过中国浙江省丽水市某HEVPP的算例分析表明,所提调度策略能够反映流域水电站的拓扑特性,充分挖掘梅雨季节水电与风光资源间的协同调度能力,优化水电站群在不同防洪风险偏好下的蓄水策略,在保证流域防洪安全的基础上提升HEVPP的运行效益。

电动汽车与电力—交通耦合网互动:综述与展望

随着电动汽车的广泛普及,交通电气化程度不断上升,配电网与交通网呈现出深度耦合的趋势。实现电动汽车与电力—交通耦合网间的高效互动,可有效提升电网运行稳定性,并缓解交通网的拥堵,因此,其成为电力系统和电动汽车领域研究的热点。全面介绍电力—交通耦合网的基本概念,并深入探讨3个关键领域:电力—交通网耦合系统建模和状态预测、两网耦合下的EV充电站规划以及两网耦合下的优化调度与控制,并分析其标准体系建设状况,对其未来的发展方向提出前瞻性的展望。

考虑生态流量的梯级水库主从博弈优化调度研究

针对传统集中式多目标优化范式未充分考虑多利益相关主体博弈关系对水库调度决策的影响,导致难以保障河道生态流量的现实问题,设置了共同保障和单独保障两种生态流量保障情景,构建了发电服从防洪调度原则下的梯级水库主从博弈优化调度模型,并在拉萨河流域旁多直孔梯级水库开展了实例研究。结果表明:梯级水库在共同保障生态流量下综合效益最优,仅由直孔水库单独保障生态流量造成调度效益难以凸显,其中防洪效益降低4.35%,整体发电效益降低1.66%;效益降低的主要原因在于单库保障模式下枯水期旁多水库倾向于蓄水而减少下泄,导致直孔水库过度下降水位来保障生态流量。

Learning the optimal power flow:Environment design matters

To solve the optimal power flow(OPF)problem,reinforcement learning(RL)emerges as a promising new approach.However,the RL-OPF literature is strongly divided regarding the exact formulation of the OPF problem as an RL environment.In this work,we collect and implement diverse environment design decisions from the literature regarding training data,observation space,episode definition,and reward function choice.In an experimental analysis,we show the significant impact of these environment design options on RL-OPF training performance.Further,we derive some first recommendations regarding the choice of these design decisions.The created environment framework is fully open-source and can serve as a benchmark for future research in the RL-OPF field.

水电站经济运行指标实时数字化研究

在传统的水电优化调度工作中,因水电站特性和管理单位的不同,存在对电站的水电经济运行管理评价方法和标准不统一的情况。该文通过介绍水电站经济运行实时数字化研究,按水电优化调度提高发电效益的两种主要途径,分别建立数学计算模型,对单电厂、梯级多厂和电网整体综合的经济运行指标进行数字化处理,分别以小时、日、月、年为阶段自动计算,实时展示优化调度结果,为综合评价水电优化调度成果提供更科学、合理、客观的数据量化指标依据,指导电网和水电厂优化调度工作。

考虑碳排放流与需求响应的电力系统两阶段优化调度

风电接入从发电侧降低了电力系统碳排放,而引入需求响应消纳弃风为负荷侧降碳提供了新思路。文中综合考虑风电和需求响应,基于电力系统碳排放流理论提出日前、日内两阶段电网低碳优化调度方法。首先,分析电力系统碳排放流理论,建立负荷侧的节点碳势模型;其次,将柔性负荷分为可转移负荷和可削减负荷,基于负荷节点碳势模型设计调用这两类负荷降碳的响应机制;然后,建立考虑低碳性和经济性的源荷协调日前优化调度模型,基于模型预测控制求解日前优化调度模型,并通过反馈校正调整日内调度结果。最后,分别通过改进PJM-5节点和IEEE 300系统进行仿真验证,证明了文中所提优化调度方法能有效促进柔性负荷消纳风电,减少弃风,同时实现负荷侧降碳。

电网调度AVC系统自动优化协调控制方法

由于电网调度过程中电压变化量较大,电网调度AVC系统难以精准控制电压变化,所以提出电网调度AVC系统自动优化协调控制方法。确定总-中-地调三个层次协同的电力系统架构,搭建协调调度控制模型,确定母线、机组、无功裕度、功率方面的协调约束规则。在协调约束规则下确定电网AVC系统分层分区结构,计算电压无功控制空间中任意两个待分区节点之间的距离,划分电压无功控制区域,结合灵敏度确定优化协调控制矩阵,实现AVC系统自动优化协调控制。实验结果表明,该方法控制下的电压与实际数值之间误差小,能够实现电压平衡和降低网损的目标。