Distributed Optimal Microgrid Energy Management With Considering Stochastic Load

Energy management in microgrids is typically formulated as a nonlinear optimization problem. Solving it in a centralized manner does not only require high computational capabilities at the microgrid central controller (MGCC), but may also infringe customer privacy. Existing distributed approaches, on the other hand, assume that all generations and loads are connected to one bus, and ignore the underlying power distribution network and the associated power flow and system operational constraints. Consequently, the schedules produced by those algorithms may violate those constraints and thus are not feasible in practice. Therefore, the focus of this paper is on the design of a distributed energy management strategy (EMS) for the optimal operation of microgrids with consideration of the distribution network and the associated constraints. Specifically, we formulate microgrid energy management as an optimal power flow problem, and propose a distributed EMS where the MGCC and the local controllers jointly compute an optimal schedule. We also provide an implementation of the proposed distributed EMS based on IEC 61850. As one demonstration, we apply the proposed distributed EMS to a real microgrid in Guangdong Province, China, consisting of photovoltaics, wind turbines, diesel generators, and a battery energy storage system. The simulation results demonstrate the effectiveness and fast convergence of the proposed distributed EMS.

In this manuscript, a hybrid method for optimum energy management of micro grid (MG) connected system is proposed. The MG connected sources are Photovoltaic (PV) array, Wind Turbine (WT), Fuel Cell (FC), Micro Turbine (MT), Diesel Generator (DG), battery storage (BESS). The proposed technique is the consolidation of Artificial Neural Network (ANN), Artificial Bee Colony (ABC), and Firefly Algorithm (FA). The proposed method is utilized to maintain the power flow (PF) amid the energy sources and grid. Initially, the predicted load requirement based on the ANN technique predicts the load requirement and ABC optimizes the MG configuration. Finally, the FA technique helps to reduce the FC including operation with maintenance cost. The proposed method is executed in MATLAB/Simulink site and the performance is analyzed with different existing methods like online management, ABC, ABC-ABC, and ABC-Gravitational Search (GS). Here, the maximal power generation of photovoltaic is 5.9 kW, wind turbine is 5.9 kW, micro turbine is 4 kW, FC is 4 kW, DG is 6 kW, and battery is 0.5 kW. The total cost of ABC, ABC-ABC, ABC-GS, and the proposed technique is about 4.1$/h, 3.9$/h, 3.3$/h, and 2.7$/h. Optimum micro grid combination using proposed method, overall cost comparison using different methods, statistical evaluation of MG management, cost accuracy percentage (CAP) of proposed and existing techniques, efficiency under various number of trails are also analyzed in this work. The comparison results demonstrate that the proposed technique has minimal cost-efficient based on its load requirement.

Developing Hybrid Demand Response Technique for Energy Management in Microgrid Based on Pelican Optimization Algorithm

Energy management in microgrids is typically formulated as an offline optimization problem for day-ahead scheduling by previous studies. Most of these offline approaches assume perfect forecasting of the renewables, the demands, and the market, which is difficult to achieve in practice. Existing online algorithms, on the other hand, oversimplify the microgrid model by only considering the aggregate supply-demand balance while omitting the underlying power distribution network and the associated power flow and system operational constraints. Consequently, such approaches may result in control decisions that violate the real-world constraints. This paper focuses on developing an online energy management strategy (EMS) for real-time operation of microgrids that takes into account the power flow and system operational constraints on a distribution network. We model the online energy management as a stochastic optimal power flow problem and propose an online EMS based on Lyapunov optimization. The proposed online EMS is subsequently applied to a real-microgrid system. The simulation results demonstrate that the performance of the proposed EMS exceeds a greedy algorithm and is close to an optimal offline algorithm. Lastly, the effect of the underlying network structure on energy management is observed and analyzed.

This paper investigates the application of ant colony optimization (ACO) for energy management in microgrids, incorporating distributed generation resources such as solar panels, fuel cells, wind turbines, battery storage, and microturbine. The study evaluates energy management in two scenarios, with the first utilizing all available resources and the second applying specific operational constraints to wind turbines and solar panels. Results demonstrate that ACO effectively reduces operational costs and enhances system reliability, contributing to sustainable energy management practices. Additionally, the study compares ACO with the imperialist competitive algorithm, highlighting ACO’s superior performance in cost reduction and efficiency improvements. This research advances energy system optimization and supports the development of sustainable solutions in microgrids.

Objective: The objective of this paper is to explore technology trends and prospects for efficient energy management in microgrids by identifying and analyzing distinct research lines in this field. Method: A Scopus search was conducted using keywords such as "microgrids" and "new technologies." The gathered information was processed using Bibliometrix software to cluster the data. This analysis identified five distinct research lines related to microgrids and energy management. Results and Discussion: The analysis revealed five distinct research lines: (1) microgrids related to energy generation and storage, (2) electric power system control, (3) electric power transmission networks, (4) optimization, and (5) power markets. The paper develops and explains these research lines comprehensively, detailing technology trends and prospects for efficient energy management. Additionally, a combined analysis of relevance and development classifies the research lines as either emerging or declining and determines whether they represent basic or motor themes. Research Implications: The findings provide insights into current and future trends in microgrid technology, offering valuable information for researchers, practitioners, and policymakers. Understanding these trends can help guide future research, development, and implementation strategies for efficient energy management in microgrids. Originality/Value: This paper offers a comprehensive analysis of technology trends and prospects in microgrids, utilizing advanced bibliometric tools to identify and classify research lines. The study provides a unique perspective on the development and relevance of different aspects of microgrid technology, contributing valuable insights to the field.

Renewable energy sources have emerged as an alternative to meet the growing demand for energy, mitigate climate change, and contribute to sustainable development. The integration of these systems is carried out in a distributed manner via microgrid systems; this provides a set of technological solutions that allows information exchange between the consumers and the distributed generation centers, which implies that they need to be managed optimally. Energy management in microgrids is defined as an information and control system that provides the necessary functionality, which ensures that both the generation and distribution systems supply energy at minimal operational costs. This paper presents a literature review of energy management in microgrid systems using renewable energies, along with a comparative analysis of the different optimization objectives, constraints, solution approaches, and simulation tools applied to both the interconnected and isolated microgrids. To manage the intermittent nature of renewable energy, energy storage technology is considered to be an attractive option due to increased technological maturity, energy density, and capability of providing grid services such as frequency response. Finally, future directions on predictive modeling mainly for energy storage systems are also proposed.

Escalating energy demands and climate change challenges necessitate the adaptation of renewable-based microgrid systems in the energy sector. The proposed work employs a robust Multi Agent System (MAS) technique to achieve efficient and automated control of the hybrid microgrid operation. The hybrid microgrid system incorporates Renewable Energy Sources (RES), a diesel generator, and a battery storage system. The operation of the hybrid microgrid consists of three distinct modes: islanded, transition to grid, and grid-oriented mode. The system’s performance is optimized by considering factors like climatic patterns, energy costs, connected source characteristics, and load demand. Different climatic scenarios are assessed for each mode of operation, where the best, extreme sunny, extreme cloudy, and worst climate conditions are considered for islanded mode; sunny and cloudy scenarios are considered for transition to grid mode as well as grid-feed and grid-tied modes are considered for grid-oriented operation of the microgrid. The simulation studies are performed using the MATLAB/Simulink R2021a environment. Furthermore, Particle Swarm Optimization (PSO) is implemented to optimize power allocation within the microgrid and enhance its cost-effectiveness. The optimization results demonstrate efficient utilization of available energy sources along with effective energy management facilitated by the MAS control system. The results emphasize the importance of adopting a MAS approach for achieving smart energy management through comprehensive analysis and integrating decentralized energy management techniques for optimal accommodation of distributed energy resources in hybrid microgrids

Energy management strategy and capacity planning of an autonomous microgrid: Performance comparison of metaheuristic optimization searching techniques

Coordinated energy management for an islanded microgrid with multi-energy and multi-storage units

Implementation of a multistage predictive energy management strategy considering electric vehicles using a novel hybrid optimization technique

In the real-time energy management of offshore islanded microgrids, determining the optimal operating points of storage systems, particularly in hydrogen-based storage, poses significant challenges. These arise from the stochastic nature of offshore Renewable Energy Sources (RES), variable power demand, and the volatility of hydrogen systems. To address these, a novel objective function has been developed that integrates electrolyzer system efficiency into the Energy Management Strategy (EMS) of a DC microgrid. Unlike most existing literature, which considers electrolyzer efficiency as a constant, this work treats efficiency as a dynamic variable that depends on operating current, temperature, and pressure. The behavior of the electrolyzer efficiency with respect to these parameters is modeled, verified, and subsequently incorporated into the EMS. A MINLP based EMS is developed to implement the proposed formulation, and its effectiveness is validated by demonstrating optimal microgrid performance under the above scenario. Notably, the impact of optimal temperature and pressure control is evidenced by electrolyzer efficiency improvements of 1.7% and 3.1% in the efficiency-focused and multi-objective scenarios, respectively. The proposed EMS is evaluated against a rule-based and heuristic (PSO) methods. In the cost-based case, the developed method shows 36.5% and 0.04% cost reductions relative to the rule-based approaches, and a 0.39% reduction relative to PSO. In the efficiency-based scheme, it attains 0.10% efficiency gain over PSO for the optimal temperature-pressure method, and in the multi-objective case, it delivers 7.40% efficiency gain versus PSO. • Novel optimization with electrolyzer efficiency for offshore islanded DC microgrids. • Electrolyzer efficiency modeled dynamically via current, temperature, and pressure. • Multi-objective framework balances cost and efficiency in flexible microgrid EMS. • Sensitivity to electrolyzer operating conditions is analyzed. • EMS validated against rule-based and metaheuristic approaches.

Optimal energy management of a PV-WTG-BSS-DG microgrid system

A hybrid method for simultaneous optimization of DG capacity and operational strategy in microgrids considering uncertainty in electricity price forecasting

This paper addresses the optimal energy management and scheduling of renewable based microgrids (MGs) in the highly correlated uncertain environments. The proposed MG incorporates different types of dispatchable and non-dispatchable units such as micro turbine (MT), fuel cell (FC), wind turbine (WT), photovoltaic panel (PV) and a battery bank (BAT). Due to the nonlinear and non-convex nature of the proposed problem, a heuristic approach based on the bird mating optimization (BMO) algorithm is employed to solve the problem. The uncertainties associated with the market price (MP), PV output power, WT output power and hourly load uncertainties are modeled using the unscented transformation (UT) method. In addition, two sampling procedures are adopted, namely as the (2m+1) and the (2m) point methods, to provide a comparative analysis showing the highly capabilities of the proposed framework. Moreover, the correlation among uncertain variables is taken into consideration and their effects on the MG's optimal operation are analyzed. The simulation results reveal the high accuracy, effectiveness, relatively timesaving and tending to avoid local minimums by the proposed model.