Energy Management Strategy For Microgrid Research Articles

Hysteresis‐based energy management strategy for a microgrid with controllable heating loads

Hysteresis-based energy management strategy for microgrid containing photovoltaic, ESS and heating loads is proposed in this study. In this real-time optimisation method, economic cost, operation cost, comfort level, renewable energy penetration and other performance indices are optimised in real time. To solve the complex energy management problem, when power forecasting is unavailable, different operational cases are clarified and calculated accordingly. In this way, the management process is turned into a finite-solution problem. Furthermore, a hysteresis loop is also established based on the cost function to optimise the system continuously, of which the loop width is offline designed. As a result, the computation burden is reduced. Finally, simulation results are presented to verify the effectiveness of the proposed energy management method. The application of the proposed method can reduce the economic cost and improve the operational performances of a microgrid significantly, including comfort, power quality and health of ESS.

Multi-agent system based sequential energy management strategy for Micro-Grid using optimal weighted regularized extreme learning machine and decision tree

Renewable energies are fundamentally changing the traditional power grid. Their integration in micro grid constitutes the best way to produce clean energy in a large scale. However, classical control methods based centralized approaches are not efficient to manage and control the different operatio ns in micro grid. In this paper, an intelligent energy management system is presented for micro grid power control based on the distributed paradigm of Multi-Agent System. Its main objective is to find the optimal control of a MG with grid-connected mode in order to control the amount of power delivered or taken from the Distribution Network so as to minimize the cost and maximize the benefit. We present also a photovoltaic and wind power prediction method using an Optimal Weighted Regularized Extreme Learning Machine algorithm in which the Particle Swarm Optimization method is used to optimize the regularization parameter. The algorithm is tested on real weather data and has shown a good generalization performance and better results than the basic Extreme Learning Machine algorithm while keeping its extremely fast training speed ability. To establish an efficient energy management strategy, a Decision Tree is used to ensure the availability of power on demand by taking a reasonable decision about charging batteries/selling electricity and discharging batteries/buying electricity in order to reduce the balance between cost and benefit.

A hybrid prediction-based microgrid energy management strategy considering demand-side response and data interruption

Data interruption may cause the centralized energy management system of a microgrid (MG) to collapse. To solve this problem, a hybrid prediction-based energy management strategy is proposed in this paper to predict interrupted data for the centralized dispatching process of an MG. This hybrid prediction method is designed following a combination of model predictive control and extreme learning machine techniques. Based on the predicted data of distributed energy resources and three types of loads from demand-side response, an optimization model is formulated to minimize the operational cost. If some predicted data are interrupted during transmission, then a prospect vulnerability assessment method is applied to select a neighboring device to predict the interrupted data. In the end, an improved particle swarm optimization algorithm is proposed with the help of genetic algorithms to accelerate convergence to global optimal solutions for the proposed MG energy management problem. The effectiveness of the proposed models and solution methods is also verified by a case study.

Energy Management Strategy for Micro-Grids with PV-Battery Systems and Electric Vehicles

This paper analyzes the impact of photovoltaic (PV) systems on storage and electric vehicles in micro-grids. As these kinds of systems are becoming increasingly popular in the residential sector, the development of a new generation of equipment, such as more efficient batteries or solar panels, makes further study necessary. These systems are especially interesting in commercial or office buildings, since they have a more repetitive daily pattern of electricity consumption, which usually occurs within the maximum solar radiation hours. Based on this need, a novel control strategy aimed at efficiently managing this kind of micro-grid is proposed. The core of this strategy is a rule-based controller managing the power flows between the grid and the batteries of both the PV system and the electric vehicle. Through experimental data and simulations, this strategy was tested under different scenarios. The selected testbed consisted of the laboratory of a research center, which could be easily scalable to the entire building. Results showed the benefits of using an electric vehicle as an active agent in energy balance, leading to a reduction of the energetic costs of a micro-grid.

Energy management strategy for microgrids including heat pump air‐conditioning and hybrid energy storage systems

The stable and economic operation of microgrids always requires the participation of hybrid energy storage systems (HESSs). In order to reduce the capacity and investment cost of HESSs, the application of heat pump air-conditioning systems (HPACSs) is a feasible solution to the problem. This study proposes a microgrid energy management strategy based on the coordination of HPACSs and HESSs. The strategy is devised to regulate the peak-valley loads on demand side and to smooth the power fluctuation of renewable energy sources in the microgrids. Peak-valley load difference is reduced through the heat capacity model of HPACS and battery energy storage system. Power fluctuation is allocated between HPACS and HESS by using fuzzy control theory. A simulation of a microgrid including HPACS and HESS demonstrates the effectiveness of the proposed strategy.

A Microgrid Energy Management System with Demand Response for Providing Grid Peak Shaving

A microgrid that consists of distributed energy sources, such as micro turbines, wind turbines, fuel cells, and photovoltaic panels, storage devices and a group of radial load feeders are quickly rising in popularity in recent years. Because there are various controllable units in a microgrid, it has the potential to provide ancillary services (A/Ss) for the grid in the grid-connected mode. This article proposes a novel microgrid energy management strategy with demand response to provide peak shaving for the grid by using the peak-time rebate scheme. Under this scheme, various controllable units, such as controllable loads, battery storage, and diesel generators, participate voluntarily in the demand-response program to achieve maximum profit for the microgrid, so the microgrid can respond actively to the grid. The optimal energy management for the microgrid is achieved under this scheme. In addition, the main grid can benefit from the scheme with a lower cost compared with other traditional peak-shaving programs. Various incentive rewards of the peak-time rebate and demand-response levels of the controllable loads are discussed in the peak-time rebate scheme. The results demonstrate that the peak power of the grid tie-line is shaved markedly while achieving optimal energy management.

Energy Management Strategy for Microgrids by Using Enhanced Bee Colony Optimization

This paper presents a microgrid (MG) energy management strategy by considering renewable energy and battery storage systems. Renewable energy, including wind power generation and solar power generation, is integrated into the distribution network, for which is formulated the optimal dispatch model of mixed-power generation by considering the charging/discharging scheduling of battery storage systems. The MG system has an electrical link for power exchange between the MG and the utility during different hours of the day. Based on the time-of-use (TOU) and all technical constraints, an enhanced bee colony optimization (EBCO) is proposed to solve the daily economic dispatch of MG systems. In the EBCO procedure, the self-adaption repulsion factor is embedded in the bee swarm of the BCO in order to improve the behavior patterns of each bee swarm and increase its search efficiency and accuracy in high dimensions. Different modifications in moving patterns of EBCO are proposed to search the feasible space more effectively. EBCO is used for economic energy management of grid-connected and stand-alone scenarios, and the results are compared to those in previous algorithms. In either grid-connected or stand-alone scenarios, an optimal MG scheduling dispatch is achieved using micro-turbines, renewable energy and battery storage systems. Results show that the proposed method is feasible, robust and more effective than many previously-developed algorithms.