Distributed Optimal Energy Management in Microgrids

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.

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

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

The uncertainty of load is a challenging issue for power systems. These uncertainties can damage the power systems and even cause blackout. Therefore, it is necessary to handle this problem. In this paper, a method for energy management in microgrids with stochastic load is proposed. The design is performed through solving a nonlinear optimization problem. In the centralized optimization technique, high computational capabilities are needed and the customer's privacy might be infringed. On the contrary, in distributed techniques, the power network and its constraints are neglected since it is assumed that all loads and generations are connected to one bus. Consequently, in the proposed distributed energy management strategy (EMS), the optimization problem is decomposed into two optimization levels, which consist of the microgrid centralized controller and local controllers and the distribution network and the corresponding constraints are taken into account. Since the precise prediction of the load is not accessible, the load is assumed stochastic. The proposed distributed EMS is integrated with diesel generators, wind turbines, photovoltaics, and a battery storage system. The simulation results depict the efficiency 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.

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

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.

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.

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.

Energy management in DC microgrid is complex and challenging due to the stochastic nature of renewable energy sources and load demand. Coping with the deficit power, peak demand, and power converter control operations are a few major concerns. The photovoltaic (PV) system and battery energy storage system (BESS) utilization need special attention for the reliable and efficient operation of the DC microgrid. Hence, this article proposes the centralized energy management scheme (CEMS) in the DC microgrid to address the abovementioned challenges. The CEMS coordinates among the various physical layer components and computational layer (i.e., control layer) of the DC microgrid. It utilizes the multioptimization for energy management during the peak and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> -peak load demand, optimal usage of PV-BESS, and performs the optimal load shedding operation. Furthermore, an optimal utilization of the PV-BESS problem is formulated and solved using linear programming to avoid excess usage of AC grid power. In deficit PV power, the optimal load shedding operation is formulated as mixed-integer linear programming problem to ensure the DC bus voltage regulation and power balance. The proposed CEMS demonstrates the effective operation of the PV system in maximum power point tracking, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> -MPPT mode (i.e., voltage control), and BESS's charging/discharging operation. It is showcased that the hierarchical control structure of CEMS improves the performance, efficiency, and reliability of the DC microgrid operation. Hence, a 48 V and 1.2 kW PV-BESS-based DC microgrid system is developed to show the efficacy of the proposed algorithm under different scenarios in MATLAB/Simulink. The real-time simulator results of the developed system, obtained using OPAL-RT OP4510, confirmed the effectiveness and reliability of the proposed scheme.

A novel approach for sizing thermal and electrical energy storage systems for energy management of islanded residential microgrid

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

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

The integration of distributed generators (DGs), electric storage system (ESS), distributed electric loads and the utility grid through the point of common coupling is called microgrid (MG) .The coordinated operation of MG and the main utility grid with variable load demand is controlled by using microgrid control center (MGCC). MGCC methodology is introduced to regulate power flow for each source depends on the outputs commands from MG energy management center (MGEMC). This paper is focused only on developing an efficient and fast MGCC based on power control mode taking into account the outputs commands from MGEMC without considering its strategy. The MG in this paper is assumed to be interconnected to the main utility gird, and can purchase some power from utility grid at off-peak hours or when the production of the MG is insufficient to meet the load demand. On the other hand, there is a daily income to the MG when the generated power exceeds the load demand during on-peak hours. The proposed MGCC and electrical power system is simulated using Matlab/Simulink.

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.