Power System Economic Dispatch Research Articles

Medium-long term load forecasting method considering industry correlation for power management

Medium-long term load forecasting is recognized as the basis of economic dispatch in power systems, and high-precision load forecasting results can provide effective support for power management. Therefore, a medium-long term load forecasting method based on the deep learning algorithm is proposed with industry correlation considered. First, the missing monthly load data is supplemented based on the iterative interpolation method, and min–max normalization method is used for data preprocessing. On this basis, the Pearson correlation coefficient is adopted for quantifying the industry correlation and selecting the strongly related industries for load forecasting. Then, a machine learning model long-short term memory (LSTM) is utilized to forecast the medium-long term industry load with the historical data of related industries. Finally, the feasibility and accuracy of the proposed method are illustrated by the case studies on 2 major industries and 30 related industries of Jiangsu Province, China. The load forecasting error shown in the simulation results of the method proposed is kept within 5%, which is much better than other traditional methods.

Economic Dispatch of Power System Considering Wind Power Integration

Economic Dispatch of Power System Considering Wind Power Integration

A multigroup marine predator algorithm and its application for the power system economic load dispatch

AbstractMarine Predator Algorithm (MPA) is an optimization algorithm inspired by the behavior of predator and prey to catch their own food. MPA is simple and easy to implement. To further improve the performance of MPA, this paper proposes a Multigroup Marine Predator Algorithm (MGMPA). The multigroup mechanism is to divide the initial population into several independent groups. These groups generate the top predator and the Elite matrix based on different strategies and share information after a fixed iteration. Above strategies include the maximum of the same group, the average of the same group, the maximum of different groups and the average of different groups. To verify its performance, the paper compares MGMPA with some classic algorithms such as Particle Swarm Optimization (PSO), Parallel Particle Swarm Optimization (PPSO), Slap Swarm Algorithm (SSA), and Marine Predator Algorithm (MPA). In addition, the proposed MGMPA is also applied to solve Economic Load Dispatch problem (ELD). The experimental results show that the proposed MGMPA has significant advantages under the CEC2013 suite and obtains the minimum cost of power system operation and the maximum economic benefits in the application.

Economic dispatch of power systems with LMP-dependent demands: A non-iterative MILP model

The proliferation of demand response programs in the smart grid provides the system operator unique opportunities to reduce the load peak and alleviate network congestions. This paper considers the economic dispatch problem with elastic demands which flexibly respond to the locational marginal prices (LMPs). However, LMP is the dual variable of optimal power flow (OPF) problem and thus is unknown before the OPF problem is solved. Without LMP, the elastic demand is unclear, and the OPF problem cannot be set up. Given this interactive nature, it is difficult to acquire the dispatch strategy as well as the LMP according to the traditional OPF method. This paper thoroughly addresses this problem. Specifically, the limitation of the traditional LMP scheme in the described situation is analyzed. An equilibrium solution may not exist because the demand function and the discontinuous LMP may not have an intersection. To overcome this difficulty, LMP at the discontinuity point is redefined, so that the dispatch problem always has an equilibrium solution. A mixed-integer linear programming model for the economic dispatch problem with LMP-dependent load is proposed, and the equilibrium solution simultaneously offers the dispatch strategy and LMPs. Case studies demonstrate the difficulties of traditional approaches and the effectiveness of the proposed method.

Economic Emission Dispatch Considering Renewable Energy Resources—A Multi-Objective Cross Entropy Optimization Approach

The conventional electrical power system economic dispatch (ED) often only pursues immediate economic benefits but neglects the harmful environment impacts of gas emissions from thermal power plants. To address this shortfall, economic emission dispatch (EED) has drawn a lot of attention in recent years. With the increasing penetration of renewable generation, the intermittence and uncertainty of renewable energy such as solar power and wind power increase the difficulties of power system scheduling. To enhance the dispatch performance with significant penetration of renewable energy, a modified multi-objective cross entropy algorithm (MMOCE) is proposed in this paper. To solve multi-objective optimization problems, a crowding–distance calculation technique and a novel external archive mechanism are introduced into the conventional cross entropy method. Additionally, the population updating process is simplified by introducing a self-adaptive parameter operator that substitutes the smoothing parameters, while the solution diversity and the adaptability in large scale systems are improved by introducing the crossover operator. Finally, a two-stage evolutionary mechanism further enhances the diversity and the rate of convergence. To verify the efficacy of the proposed MMOCE, eight benchmark functions and three different test systems considering different mixes of renewable energy sources are employed. The dispatch results by the proposed MMOCE are compared with other multi-objective cross entropy algorithms and published heuristic methods, confirming the superiority of the proposed MMOCE over other methods in all test systems.

Bi-level Coordinated Optimal Dispatching Method of Power System Considering Renewable Energy Uncertainty and Flexible Load Aggregators

The increase of renewable energy penetration and the diversity of flexible loads in the customer side have posed new challenges to secure and economic dispatching of power system. Based on the concept of load aggregator (LA), we propose a bi-level optimal dispatching model for power system. In order to guarantee the security of power system operation, we propose a bi-level coordinated dispatching method to consider the aggregation of various flexible loads and the uncertainty of renewable energy generation, which can realize efficient and economic dispatch of power system. Numerical results in one test system demonstrate that our proposed dispatching model can reduce the total dispatch cost and LA’s electricity bill simultaneously.

Optimal Scheduling of Pumped Storage in Power System with Large-scale Photovoltaic Based on Carbon Emissions Trading

In order to improve the photovoltaic penetration of the power system, an optimal scheduling model of pumped storage system with large-scale photovoltaic based on carbon trading is proposed in this paper. Firstly, based on the analysis of low-carbon economy, the stepped carbon emission trading mechanism is introduced into the economic dispatch of power system. And in order to improve the calculating efficiency, an improved K-means clustering algorithm based on maximum and minimum distance criterion is proposed to cluster the photovoltaic power generation scenarios. Then the optimal scheduling model is established. Take the lowest comprehensive operation costs of the system as the objective function, and the operation economy and low carbon of the system are considered. The pumped storage is used as energy storage for peak load shaving in the generation system. At last, an improved IEEE RTS-96 system is taken to finish the case study, and the simulation results show that the model proposed in this paper is reasonable and effective.

Multi‐objective economic dispatch of power system with wind farm considering flexible load response

The uncertainty of wind farm power and load both have a certain impact on the economic dispatch of the power system. First, this study deals with the fuzzy processing of power prediction error and load forecasting error of wind farms based on fuzzy random theory. On this basis, a multi-objective fuzzy stochastic dispatch model of a power system with flexible load is established under the carbon trading mechanism. And, the nonlinear cost of flexible load response and the cost of carbon emission compensation is introduced to the multi-objective function of the model. In addition, the fuzzy chance constraint of the spinning reserve is added to the constraint conditions. By introducing the variable, the fuzzy random dispatch model is transformed into a clear equivalent model. Finally, the discrete bacterial colony chemotaxis algorithm is used to process the model, and the optimal solution to the multi-objective function is obtained by a compromise strategy based on a small degree of satisfaction. In the simulation, a classic IEEE10 system and a wind farm are taken as examples. The results show that compared with the other three traditional dispatch models, the total power generation cost is reduced by US$13 670, US$5610 and US$86 010, respectively using the model proposed in this article.

Distributed optimal resource allocation over strongly connected digraphs: A surplus-based approach

In this brief, a surplus-based approach is proposed to solve the distributed optimal resource allocation problem over directed graphs. Two features distinguish our method from the existing distributed resource allocation schemes. The first one is that the proposed method can be implemented on general strongly connected graph and overcomes the asymmetry caused by the digraphs. The second one is that the proposed method enjoys the privacy-preserving property and avoids the privacy information leakage in the process of information exchange. The distributed algorithm is designed and analyzed by virtue of primal–dual methods, projected gradient, and eigenvalue perturbation theory. Furthermore, distributed economic dispatch in power system, as a simulation example, is used to illustrate the effectiveness of the presented distributed algorithm.

Risk-averse robust interval economic dispatch for power systems with large-scale wind power integration

Risk-averse robust interval economic dispatch for power systems with large-scale wind power integration

Frequency-Voltage Characteristics Concerned Optimal Dispatch

The rapid development of integrated wind power bring serious challenges to power system operation. In order to address the increasing wind power curtailment problem, the frequency-voltage characteristics concerned optimal dispatch for power system with large-scale wind power integration is proposed in this paper. Through integration of the optimization of frequency characteristics and voltage characteristics into the traditional power system economic dispatch model, the flexibility resources of generation side and load sides in power systems are mobilized to promote large-scale wind power accommodation. The formulated frequency-voltage characteristics concerned optimal dispatch mathematical model is a nonlinear programming model, which is solved by the GAMS platform. The numerical analysis for an actual provincial power grid indicates that the proposed frequency-voltage characteristics concerned optimal dispatch method can promote wind power utilization efficiency and improve the economy of power system operation.

Multi-objective optimization of space adaptive division for environmental economic dispatch

With the increasingly severe global energy and environmental issues, the traditional economic load dispatching with the single goal of power generation cost has been unable to meet the national macro strategic requirements for energy conservation and emission reduction. This research mainly discusses the multi-objective optimization of spatial adaptive partitioning based on environmental economic dispatch problems. This study uses the method of calculating power generation costs and pollution control costs to select a compromise solution. When initializing the individual optimal position memory bank and the global optimal position memory bank, put the first-generation particle position into the individual optimal position memory bank, and put the optimal position obtained by comparing the first-generation particle into the global optimal position. In the location memory. Then calculate the fitness value corresponding to each objective function of each particle, compare the calculated result with the corresponding results of each Pareto solution in the internal memory bank and the external memory bank, and replace the particle position with the best. In the multi-objective processing strategy, in order to better obtain the Pareto optimal solution set and the Pareto optimal frontier, an external archive is set up to save and update the new elite Pareto optimal solution generated after each iteration. Since the quality of the particles in the random simulation particle swarm algorithm will directly affect the convergence and diversity of the final Pareto optimal solution set, this study selects the historical optimal particles according to the Pareto dominance relationship. When the multi-objective optimized power system economic dispatch research model with wind farms was adopted, the cost of power generation increased by 6.21 million yuan, the cost of polluting gas treatment was reduced by 8.1 million yuan, and the total cost was reduced by 1.89 million yuan. The research results show that the multi-objective optimization model proposed in this research has achieved a relatively satisfactory balance between power generation costs and environmental advantages.

An Effective Algorithm for Economic Dispatch of Power System with Prohibited Operating Zone

In the actual operation of the power system, some generating units may have physical limitations. For a unit with prohibited operating zone, its operation zone [Pmin, Pmax] will be divided into several discrete sub feasible regions. In this paper, the influence of units with prohibited operating zone on the system is deeply studied, and the influence of units with prohibited operating zone on other units in the system, including the influence between units with prohibited operating zone, and the optimal solution state of economic dispatch problem for units with prohibited operating zone are further studied. Then the iterative algorithm for the above three problems is modified. At the same time, the economic law and physical essence of the economic dispatch problem for units with prohibited operating zone are explored. With high calculation efficiency, the algorithm proposed in this paper has its own engineering application value.

Energy management system for two islanded interconnected micro-grids using advanced evolutionary algorithms

An efficient approach to optimal energy management and determine daily optimal operation schedule of two interconnected micro-grids (MGs), isolated from the grid, is proposed. The proposed energy management system consists of day-ahead and hour-ahead scheduling, and economic dispatch during real-time operation. The optimal day-ahead unit commitment can be achieved through two stages, management of the power generated from sources in both MGs and controllable load management. The day-ahead energy scheduling of each unit with technical constraints, being a complex nonlinear problem with several inequality constraints, needs to be optimized to achieve high quality operation as well as minimum daily forecasted energy consumption cost. Meta-heuristic algorithms seem more suitable to handle the task of optimal scheduling compared to the conventional analytic methods for power system economic dispatch. Considering the technical constraints, an advanced state-of-the-art meta-heuristic optimization technique, a modified version of the basic Porcellio Scaber algorithm (PSA) that offers improved efficiency in minimizing the objective function, is applied to solve the optimization problem. Results obtained demonstrate that the developed Global PSA is more efficient than a number of other meta-heuristic techniques to determine the optimal economic dispatch of multi-micro-grids incorporating various types of distributed generators.

An enhanced multi‐objective differential evolution algorithm for dynamic environmental economic dispatch of power system with wind power

AbstractDynamic environmental economic dispatch (DEED) with wind power is an important extension of the classical environmental economic dispatch (EED) problem, which could provide reasonable scheduling scheme to minimize the pollution emission and economic cost at the same time. In this study, the combined dynamic scheduling of thermal power and wind power is carried out with pollutant emission and economic cost as optimization objectives; meanwhile, the valve‐point effect, power balance, ramp rate, and other constraints are taken into consideration. In order to solve the DEED problem, an enhanced multi‐objective differential evolution algorithm (EMODE) is proposed, which adopts the superiority of feasible solution (SF) and nondominated sorting (NDS) two selection strategies to improve the optimization effect. The suggested algorithm combines the total constraint violation and penalty function to deal with various constraints, due to different constraint techniques could be effective during different stages of searching process, and this method could ensure that each individual in the Pareto front (PF) is feasible. The results show that the proposed algorithm can deal with DEED problem with wind power effectively, and provide better dynamic scheduling scheme for power system.

Multiverse optimisation based technique for solving economic dispatch in power system

Economic dispatch (ED) is one of the many important components in a power system operation. It is designed to calculate the exact amount of power generation needed to ensure a minimum cost of generation. A power system with multiple generators should be running under an economic condition. The operating cost has to be minimised for any feasible load demand. The increase of power demand is getting higher throughout the year. Economic dispatch is used to schedule and control all output of the fossil-fuel or coal-generators to satisfy the system load demand at a minimum cost. This paper presents the Multiverse Optimisation (MVO) for solving the economic dispatch in a power system. The proposed Multiverse optimisation engine developed in this study is implemented on the IEEE 30-Bus Reliability Test System (RTS). It has five generators, all of which are denoted as the control variables for the optimisation process. To reveal the superiority of MVO, a similar process was conducted using Evolutionary Programming (EP). Results from both techniques were compared, and it was revealed that MVO had outperformed EP in terms of reduced cost of generation for the system.

Multi-objective fuzzy optimization model of power system dynamic environment economy based on coordinated dispatching of wind power and electric vehicles

In order to coordinate the impact of the integration of electric vehicles and wind power on the economic dispatch of power systems, a multi-objective dispatch model for the environmental economy of wind farms with PHEVs connected to the grid was constructed. The uncertain factors of wind power generation and load will increase the difficulty of dispatching. The fuzzy theory will be used to realize the fuzzy modelling of the coordinated connection of wind power and electric vehicles. The satisfaction index will be used to transform the multi-objective problems of deterministic environment and economy into single For the objective optimization problem, based on the improvement of the traditional particle swarm optimization algorithm using linear descent search ideas and the particle information sharing method considering boundary constraints, the improved PSO algorithm is used to solve the proposed dynamic environment and economic dispatch model. From the grid operation and vehicle owners from the perspective of demand, the constraints of the system’s rotation reserve and the daily travel of the owner are considered. The comparison and analysis of the simulation of algorithms and calculation examples and multiple scheduling schemes in different scenarios verify the rationality of the model and the superiority of the improved PSO algorithm for solving such dynamic scheduling problems.

Stochastic economic dispatch of power system with multiple wind farms and pumped‐storage hydro stations using approximate dynamic programming

The stochastic economic dispatch problem of power system with multiple wind farms and pumped-storage hydro stations is formulated as a specific stochastic dynamic programming (DP) model, i.e. stochastic storage model, it is impossible to obtain an accurate solution due to the curse of dimensionality. Based on the approximate DP (ADP) method, the stochastic storage model can be transformed into a series of mixed-integer linear programming (MILP) models by describing the approximate value functions (AVFs) as convex piecewise linear functions in post-decision states. The AVFs are first initialised using the results of the deterministic model under a forecast scenario of wind farm output and then trained by scanning stochastic sampling scenarios consecutively with the successive projective approximation routine algorithm. To obtain a near-optimal day-ahead dispatch scheme, the forecast scenario is substituted into the MILP models expressed by the trained AVFs and is solved forward through each time interval. The network constraints are incorporated by the while-loop detection of critical lines. Test results on an actual provincial power system and the modified IEEE 39-bus system, including the comparison among the ADP, DP, scenario-based and chance-constrained programming methods, demonstrate the feasibility and efficiency of the proposed model and algorithm.

Multi-Step Ahead Short-Term Load Forecasting Using Hybrid Feature Selection and Improved Long Short-Term Memory Network

Short-term load forecasting (STLF) plays an important role in the economic dispatch of power systems. Obtaining accurate short-term load can greatly improve the safety and economy of a power grid operation. In recent years, a large number of short-term load forecasting methods have been proposed. However, how to select the optimal feature set and accurately predict multi-step ahead short-term load still faces huge challenges. In this paper, a hybrid feature selection method is proposed, an Improved Long Short-Term Memory network (ILSTM) is applied to predict multi-step ahead load. This method firstly takes the influence of temperature, humidity, dew point, and date type on the load into consideration. Furthermore, the maximum information coefficient is used for the preliminary screening of historical load, and Max-Relevance and Min-Redundancy (mRMR) is employed for further feature selection. Finally, the selected feature set is considered as input of the model to perform multi-step ahead short-term load prediction by the Improved Long Short-Term Memory network. In order to verify the performance of the proposed model, two categories of contrast methods are applied: (1) comparing the model with hybrid feature selection and the model which does not adopt hybrid feature selection; (2) comparing different models including Long Short-Term Memory network (LSTM), Gated Recurrent Unit (GRU), and Support Vector Regression (SVR) using hybrid feature selection. The result of the experiments, which were developed during four periods in the Hubei Province, China, show that hybrid feature selection can improve the prediction accuracy of the model, and the proposed model can accurately predict the multi-step ahead load.

Sparse Oblique Decision Tree for Power System Security Rules Extraction and Embedding

Increasing the penetration of variable generation has a substantial effect on the operational reliability of power systems. The higher level of uncertainty that stems from this variability makes it more difficult to determine whether a given operating condition will be secure or insecure. Data-driven techniques provide a promising way to identify security rules that can be embedded in economic dispatch model to keep power system operating states secure. This paper proposes using a sparse weighted oblique decision tree to learn accurate, understandable, and embeddable security rules that are linear and can be extracted as sparse matrices using a recursive algorithm. These matrix rules can then be easily embedded as security constraints in power system economic dispatch calculations using the Big-M method. Tests on several large datasets with high renewable energy penetration demonstrate the effectiveness of the proposed method. In particular, the sparse weighted oblique decision tree outperforms the state-of-art weighted oblique decision tree while keeping the security rules simple. When embedded in the economic dispatch, these rules significantly increase the percentage of secure states and reduce the average solution time.