Optimization strategies for Microgrid energy management systems by Genetic Algorithms

Abstract

Grid-connected Microgrids (MGs) have a key role for bottom-up modernization of the electric distribution network forward next generation Smart Grids, allowing the application of Demand Response (DR) services, as well as the active participation of prosumers into the energy market. To this aim, MGs must be equipped with suitable Energy Management Systems (EMSs) in charge to efficiently manage in real time internal energy flows and the connection with the grid. Several decision making EMSs are proposed in literature mainly based on soft computing techniques and stochastic models. The adoption of Fuzzy Inference Systems (FISs) has proved to be very successful due to their ease of implementation, low computational run time cost, and the high level of interpretability with respect to more conventional models. In this work we investigate different strategies for the synthesis of a FIS (i.e. rule based) EMS by means of a hierarchical Genetic Algorithm (GA) with the aim to maximize the profit generated by the energy exchange with the grid, assuming a Time Of Use (TOU) energy price policy, and at the same time to reduce the EMS rule base system complexity. Results show that the performances are just 10% below to the ideal (optimal) reference solution, even when the rule base system is reduced to less than 30 rules.

An Energy Management System Design Using Fuzzy Logic Control: Smoothing the Grid Power Profile of a Residential Electro-Thermal Microgrid

“…In this context, the FLC parameter adjustment, for instance, selection of membership functions (MF) number, type, mapping, and rule base, is performed by an offline adjustment procedure, which is described in [44] to minimize the magnitude of the defined quality criteria. Note that beside the procedure used in this work, different methods to perform the parameter adjustment of the FLC controller can be used, for instance, metaheuristic nature-inspired algorithms (i.e., Cuckoo Search, Particle Swarm Optimization, among others) [53]- [55] or evolutionary algorithms (i.e., Genetic Algorithms, Machine Learning, among others) [42], [43].…”

“…Due to its heuristic nature, this procedure was especially suitable to be improved by using expert knowledge rule-based systems based on FLC [41]. FLC have proven to be of easy implementation and low computational run time cost than other sophisticated analytical solutions such as those reported in [42], [43]. As a result, an FLC-based EMS of only 25 rules with an offline FLC parameters tuning, using measured RES generation and load consumption data for one year, was suggested in [44] and [10].…”

Energy management strategy for solid‐state transformer‐based solar charging station for electric vehicles in smart grids

“…The algorithm for implementing the EMS process is shown in Fig. 1, the main steps of which are [64–67]: (i) Obtain and calculate the required parameters. (ii) Perform the classification of EVs and EBC models. (iii) Send energy range information to the picker. (iv) Receive power planning command from the picker. (v) Implementation of the CPAA model for EVs based on power planning instructions and EV classification results. …”

Real-Time Optimal Operation Control of Micro Energy Grid Coupling With Electricity- Thermal -Gas Considering Prosumer Characteristics

“…Hydropower units are connected by 10kV or lower voltage level, and are generally small capacity units of run-of-river type which will not affect the ecological flow and irrigation of rivers, so it is not necessary to consider the cost of abandoned water. The operation cost of hydropower units (C hydro MEG-S ) mainly refers to the power consumption cost of hydropower station, as shown in (29). P hydro MEG-S is the output of hydropower unit at time t. γ ℎ is the generation cost factor of hydropower.…”

“…Mixed-Integer Second-Order Cone Programming (MISOCP) method is used by reference [28] to achieve microgrid dayahead operation. In [29] and [30], the operation of MG is optimized by using the hierarchical genetic algorithm and multi objective evolution algorithm respectively. Nowadays, the idea and method of machine learning is developing rapidly, and it is gradually used to solve the problems in power system.…”