Economic Dispatch Of Microgrid Research Articles

The Study of an Improved Particle Swarm Optimization Algorithm Applied to Economic Dispatch in Microgrids

The Study of an Improved Particle Swarm Optimization Algorithm Applied to Economic Dispatch in Microgrids

Physics-informed convolutional neural network for microgrid economic dispatch

The variability of renewable energy generation and the unpredictability of electricity demand create a need for real-time economic dispatch (ED) of assets in microgrids. However, solving numerical optimization problems in real-time can be incredibly challenging. This study proposes using a convolutional neural network (CNN) based on deep learning to address these challenges. Compared to traditional methods, CNN is more efficient, delivers more dependable results, and has a shorter response time when dealing with uncertainties. While CNN has shown promising results, it does not extract explainable knowledge from the data. To address this limitation, a physics-inspired CNN model is developed by incorporating constraints of the ED problem into the CNN training to ensure that the model follows physical laws while fitting the data. The proposed method can significantly accelerate real-time economic dispatch of microgrids without compromising the accuracy of numerical optimization techniques. The effectiveness of the proposed data-driven approach for optimal allocation of microgrid resources in real-time is verified through a comprehensive comparison with conventional numerical optimization approaches.

Privacy-Preserving Distributed Economic Dispatch for Microgrids Based on State Decomposition With Added Noises

Privacy-Preserving Distributed Economic Dispatch for Microgrids Based on State Decomposition With Added Noises

Privacy-Preserving Distributed Economic Dispatch of Microgrids Using Edge-Based Additive Perturbations: An Accelerated Consensus Algorithm

Privacy-Preserving Distributed Economic Dispatch of Microgrids Using Edge-Based Additive Perturbations: An Accelerated Consensus Algorithm

Short-term load prediction with LSTM and FCNN models Based on attention mechanisms

To address the significant load prediction discrepancies in microgrid economic dispatch, which result from substantial load fluctuations, a method for near-term load forecasting leveraging the attention mechanism is proposed, utilizing both LSTM and FCNN models. In this method, the attention mechanism is introduced to enhance the LSTM model’s ability to recognize and utilize key information, and the LSTM is used to process historical load data with time continuity selected by a sliding window, whereas the FCNN is used to process daily static features, such as the day’s maximum temperature, minimum temperature, and weather conditions. The outputs of the two networks are subsequently fused into a fully connected layer to generate the final load forecast. Training leverages six months of actual power load values and regional meteorological factor data. The forecast outcomes are then evaluated against both traditional and machine learning algorithms. The experimental findings highlight the superior prediction accuracy of the proposed model.

Quantized Distributed Economic Dispatch for Microgrids: Paillier Encryption–Decryption Scheme

Quantized Distributed Economic Dispatch for Microgrids: Paillier Encryption–Decryption Scheme

Privacy-Preserving Distributed Economic Dispatch of Microgrids Over Directed Networks via State Decomposition: A Fast Consensus Algorithm

Privacy-Preserving Distributed Economic Dispatch of Microgrids Over Directed Networks via State Decomposition: A Fast Consensus Algorithm

Microgrid Management Strategies for Economic Dispatch of Electricity Using Model Predictive Control Techniques: A Review

In recent years, microgrid (MG) deployment has significantly increased, utilizing various technologies. MGs are essential for integrating distributed generation into electric power systems. These systems’ economic dispatch (ED) aims to minimize generation costs within a specific time interval while meeting power generation constraints. By employing ED in electric MGs, the utilization of distributed energy resources becomes more flexible, enhancing energy system efficiency. Additionally, it enables the anticipation and proper utilization of operational limitations and encourages the active involvement of prosumers in the electricity market. However, implementing controllers and algorithms for optimizing ED requires the independent handling of constraints. Numerous algorithms and solutions have been proposed for the ED of MGs. These contributions suggest utilizing techniques such as particle swarm optimization (PSO), mixed-integer linear programming (MILP), CPLEX, and MATLAB. This paper presents an investigation of the use of model predictive control (MPC) as an optimal management tool for MGs. MPC has proven effective in ED by allowing the prediction of environmental or dynamic models within the system. This study aims to review MGs’ management strategies, specifically focusing on MPC techniques. It analyzes how MPC has been applied to optimize ED while considering MGs’ unique characteristics and requirements. This review aims to enhance the understanding of MPC’s role in efficient MG management, guiding future research and applications in this field.

Demand response based microgrid's economic dispatch

The development of energy management tools for next-generation Distributed Energy Resources (DER) based power plants, such as photovoltaic, energy storage units, and wind, helps power systems be more flexible. Microgrids are entities that coordinate DERs in a persistently more decentralized fashion, hence decreasing the operational burden on the main grid and permitting them to give their full benefits. A new power framework has emerged due to the integration of DERs-based microgrids into the conventional power system. With the rapid advancement of microgrid technology, more emphasis has been placed on maintaining the microgrids' long-term economic feasibility while ensuring security and stability. The objective of this research is to provide a multi-objective economic operation technique for microgrids containing air-conditioning clusters (ACC) taking demand response into account. A dynamic price mechanism is proposed, accurately reflecting the system's actual operational status. For economic dispatch, flexible loads and air conditioners are considered demand response resources. Then, a consumer-profit model and an AC operating cost model are developed, with a set of pragmatic constraints of consumer comfort. The generation model is then designed to reduce the generation cost. Finally, a microgrid simulation platform is developed in MATLAB/Simulink, and a case is designed to evaluate the proposed method's performance. The findings show that consumer profit increases by 69.2% while ACC operational costs decrease by 18.2%. Moreover, generation costs are reduced without sacrificing customer satisfaction.

A Review of Research on Dynamic and Static Economic Dispatching of Hybrid Wind–Thermal Power Microgrids

As fossil energy is increasingly depleted, promoting the integration of renewable energy into the grid and improving its utilization rate has become an irresistible development trend in China’s power industry. However, the volatility of wind power increases the difficulty of economic dispatch in power systems. With the rising participation of wind power in the system, the complexity of traditional microgrid dynamic scheduling problems has increased, transforming into a dynamic economic scheduling problem for wind power thermal power hybrid microgrids. Starting from the concept and research significance of economic dispatch, this article analyzes the current research status of microgrid economic dispatch as well as the impact and influencing factors of wind energy grid connection on it. It summarizes the research performed by scholars in two aspects: scheduling models and solving algorithms in static dispatch, as well as how to deal with wind power randomness in dynamic dispatch and how to balance environmental protection while ensuring economic maximization. Finally, the existing problems in current research were summarized and future development directions were prospected. This research has important application prospects in improving the economy of the system and protecting the ecological environment.

Integrated Energy Microgrid Economic Dispatch Optimization Model Based on Information-Gap Decision Theory

To address the problems of “difficult to consume” renewable energy and the randomness of power output, we propose the CHP unit joint-operation model with power to gas (P2G) and carbon capture system (CCS) technologies and analyze the operation cost, carbon emission, and “electric-heat coupling” characteristics of this model. A dispatch optimization model is constructed based on the information-gap decision theory under the strategy to further consider the interval uncertainty of renewable energy unit output and load forecast. The optimized-dispatching model effectively solves the fate of renewable unit output and electric-thermal load and provides dispatching strategies for decision-makers to balance risk and capital management.

Economic dispatch for microgrids with communication delays and flexible load

The consensus algorithm is widely used in the microgrid, but it is affected by the communication of distributed generators. If the communication delay is serious, it can affect the system’s performance and cause the system’s instability. To ensure the stability of the microgrid under communication constraints, aiming at the extensive penetration of the flexible loads in the microgrid, the transmission delay of the communication network is considered, this paper proposes a microgrid distributed economic dispatch method based on the consensus algorithm under communication constraints. By coordinating the information exchanges between the generator and the total load demand, the flexible load and output of the generator are optimized in the system. Compared with the traditional consensus control strategy, this strategy can ensure that the system has higher stability and convergence speed under the same delay. Finally, the IEEE-14 node system is used to verify the effectiveness and correctness of the proposed algorithm.

Economic dispatch in micro-grids with alternative energy sources and batteries

The problem of economic dispatch in a micro-grid interconnected to the conventional electrical system consists of optimizing the operating cost to satisfy the different operating requirements and the demand for electrical power required by the users. In the development of this proposal, there are challenges caused by the random nature of electricity generation systems based on renewable energy sources, for an adequate operation of the micro-grid, which can be solved through systems Energy Storage System (BESS). This paper addresses the problem of optimal economic dispatch in a microgrid that includes an energy storage system based on a battery bank and interconnected to the main grid through a mathematical programming approach, considering the benefits for customers by reducing their demand of electrical power and taking into account the costs of treatment pollutants produced by conventional energy sources. The generation systems used to satisfy the energy demand of the customers within the interconnected micro-grid are: a) conventional diesel generators, b) wind generation systems, c) photovoltaic systems, d) a hydroelectric turbine, and e) a storage system based on electric batteries. The results of the effectiveness of the proposed mathematical model show that the demand for electrical power to the main grid can be reduced and customers obtain economic benefits by integrating the incentive-based demand response program into the energy management problem when environmental contingency events occur. The results show that when there are peaks in energy demand, the operating costs of the micro-grid increase by up to 380%. In the same way, when there are failures in renewable energy sources, storage systems (BESS) and diesel generators work at 100% of their capacities. The proposal is implemented in Lingo 17.0 software and simulation results show the feasibility of the proposal through the optimization of the operation of the micro-grid.

Distributed Economic Dispatch for Microgrids Tracking Ramp Power Commands

When in grid-connected mode of operation, distributed generators (DGs) within the microgrid (MG) can coordinate to act as a single entity to provide services to the bulk grid. The DGs can coordinate their power production to minimize the total operating cost, which is known as the distributed economic dispatch. Various methods have been proposed to solve the MG economic dispatch problem (EDP) in a distributed fashion, under the assumption that DGs’ power output, in aggregate, follows a constant or slowly varying power command. However, when the MG is providing frequency regulation service, or the internal load is highly dynamic, the MG EDP becomes dynamic, and the state-of-the-art distributed approaches cannot guarantee optimality. In this paper, we propose a distributed economic dispatch algorithm for MGs providing frequency regulation service, as an example of a dispatch profile with ramp commands. A consensus protocol guaranteeing zero steady-state error for ramp inputs is integrated into the EDP to find the optimal solution in a distributed way. With the proposed algorithm, the MG is able to tightly follow a time-varying regulation signal while maintaining an optimal economic dispatch for all the DGs within. We validate the proposed method using regulation signals from PJM and demonstrate the algorithm on a hardware-in-the-loop testbed.

A Two-Stage Deep Learning Approach for Solving Microgrid Economic Dispatch

A Two-Stage Deep Learning Approach for Solving Microgrid Economic Dispatch

Multi-agent-based collaborative regulation optimization for microgrid economic dispatch under a time-based price mechanism

• This paper provides a multi-agent based coordinated dispatch strategy for the economic dispatch of the microgrid under a time-based price mechanism. • We develop a microgrid optimization model for the microgrid operation process, which includes battery regulation and user satisfaction. • The established optimization model is solved using a MACPSO algorithm, and the agent communication mechanism in the microgrid is examined. • The performance comparison using different algorithms (PSO, IPSO, MAPSO and MACPSO) has been presented. • The proposed multi-agent coordinated dispatch strategy is validated by designing a multi-agent system simulation environment and using the Java agent development (JADE) framework. The economic optimal dispatch of a microgrid is a challenging task with significant economic and social implications. Under a time-based price mechanism, this paper proposes a multi-agent-based coordinated dispatch strategy for the microgrid's economic dispatch. The information between the agents in the microgrid can be fed back in time to reduce microgrid operation failures by coordinated control between the demand-side load, the battery, and the power generation side micro-sources. The output of other micro-sources is then coordinated using an appropriate battery charging and discharging plan based on the characteristics of the time-based electricity price mechanism. Following that, an economical microgrid operation model is established and solved using a multi-agent chaotic particle swarm optimization (MACPSO) algorithm, which considers user satisfaction. Finally, a multi-agent system (MAS) simulation environment is built using the Java agent development (JADE) framework. The proposed microgrid with multi-agent coordinated control can effectively improve its operation efficiency, according to a comparative analysis of examples.

Study on Economic Dispatch of the Combined Cooling Heating and Power Microgrid Based on Improved Sparrow Search Algorithm

The reasonable and efficient use of the abundant biomass resources in rural areas has not been realized. Therefore, the concept of a combined cooling, heating, and power (CCHP) microgrid system, considering biomass pyrolysis and gasification, has been developed by researchers. A biomass gasification device can fully use biomass resources and can play a role in absorbing wind energy. Meanwhile, in order to minimize the operating cost of each micropower supply unit, as well as the environmental pollution costs, researchers have also established an optimal scheduling model for CCHP microgrids, which uses the sparrow search algorithm. In this paper, we have improved upon the traditional sparrow algorithm to solve the problems of its uneven population distribution, poor global search ability, and the risk of falling into local optima, through the development of the random walk sparrow search algorithm (RSSA). First, a sinusoidal chaotic map is used to generate the early-generation sparrow population with a uniform distribution in space. Second, in this study we add a sharing factor to the discoverer’s optimization process to enhance information sharing and the global research capability among individuals in this field. Finally, a random walk strategy is used to form new participants to improve the algorithm’s skill in locally searching for optimal locations. Taking the CCHP microgrid with grid-connected action as a case study, we concluded that compared with the optimization outcomes of the SSA, the total costs incurred by RSSA in summer and winter were reduced by 2.2% and 3.1%, respectively. Compared with the optimization findings for the chaotic SSA algorithm, the total costs incurred using the RSSA algorithm under typical summer and winter days were reduced by 0.14% and 0.13%, respectively. The productiveness of the RSSA algorithm for solving the CCHP microgrid economic dispatch issues has thus been verified.

Economic Dispatch of Microgrid Based on Load Prediction of Back Propagation Neural Network–Local Mean Decomposition–Long Short-Term Memory

To plan the work of power generation equipment, it is necessary to ensure that the power supply is sufficient and to achieve the minimum cost to ensure the safety and economy of the microgrid. Based on back propagation neural network–local mean decomposition–long short-term memory (BPNN–LMD–LSTM) load prediction, the design is based on a fixed-time consistency algorithm with random delay to predict the economic dispatch of microgrids. Firstly, the initial power load prediction sequence is obtained by continuous training of the back propagation neural network (BPNN); the residual sequence with other influencing factors is decomposed by local mean decomposition (LMD); and the long short-term memory neural network (LSTM) is used to predict the output prediction residual sequence, and the final short-term power load prediction is obtained. Based on predicting load, the fixed-time consistency algorithm with random delay is used to add supply and demand balance constraints to optimize the power distribution of the power generation units of the distributed microgrid and reduce the power generation cost of the microgrid. The results show that the prediction model has better prediction accuracy, and the scheduling algorithm based on the prediction model has a faster convergence rate to reach the lowest power generation cost.

Adaptive Dynamic Programming and Its Application to Economic Dispatch in Microgrid: A Brief Overview

Both adaptive dynamic programming and other intelligent algorithms can solve the economic dispatch problem in the microgrid. Adaptive dynamic programming can reduce the computational burden, which the intelligent algorithms suffer from, by using function approximation structure to approximate performance index function. In recent years, it has been also widely used in economic dispatch in the microgrid. In this article, we introduce some recent research trends within the field of adaptive dynamic programming based economic dispatch. Adaptive dynamic programming is firstly reviewed. Then, the current research works about adaptive dynamic programming based economic dispatch are summarized and compared. Furthermore, we point out some topics for future studies.

Deep Learning Optimization of Microgrid Economic Dispatch and Wireless Power Transmission Using Blockchain

The purpose is to realize the decentralized microgrid economic dispatch, improve the information transparency and security of microgrid systems, and make the power grid move towards a clean, safe, efficient, and reliable development path. Deep learning optimization of microgrid economic dispatch and wireless power transmission based on blockchain technology are studied. First, the related theories and methods of microgrid systems, wireless power transmission, and deep learning optimization based on blockchain technology are introduced. Next, the microgrid economic dispatch is simulated and analyzed on a large scale. Finally, the comparison results between microgrid economic dispatch and common radio energy transmission technologies are analyzed. The results show that daily planning can better coordinate the state of distributed generation, energy storage system, and public connection. The operation results of the previous day correspond to the long-term operation economy of the microgrid. The total operation cost of the microgrid is 4668 yuan/day, and the remaining power is maintained between 500 and 600 kW, which helps to prevent excessive battery discharge, prolong battery life, and reduce operation cost. The simulation results show that the total power imbalance of the microgrid can reduce the output fluctuation of controllable load shedding of distributed generation. When the load characteristics are not important, the output fluctuation of controllable distributed generation can be reduced. The proposed economic dispatch model can optimize the data security, information storage, and information release of the microgrid and has a certain guiding role for the development of the national power grid and power industry.