Dynamic Scheduling of Energy Resources in Microgrid Using Grey Wolf Optimization

A behavior-centered framework for real-time control and load-shedding using aggregated residential energy resources in distribution microgrids

Scheduling of multi-fuel energy resources in microgrid using grey wolf optimisation

Due to the high penetration of renewable energy resources in microgrids (MGs), the grid inertia becomes low which leads to the grid to be vulnerable to large disturbances. The energy storage devices can play an important role to enhance the inertia of MGs. However, due to the high investment cost of storages or their <italic>dp/dt</italic> limitation, the installed energy storages cannot cover the challenge of high <italic>df/dt</italic>. A prominent solution to solve the problem is to use the inertia response of the wind generators. However, relatively high second frequency nadir is the main drawback of using the inertia response of the wind generators which may impose an extensive disturbance to MGs. Accordingly, a coordinated operation strategy for MGs between wind generator and hybrid energy storage (HES) system is proposed in this paper. In addition, to improve the inertia response of the MG; providing high-quality communication infrastructures with low delay and increasing the Ultracapacitor capacity have been paid attention. In this paper, the costs of the installed Ultracapacitor and quality of communication services are defined as the operation cost. Guaranteeing enough frequency damping for the MG with low operation cost are two conflict objectives. Therefore, a multi-objective optimization method is used to set the controllers&#x2019; values and reduce the operation cost. The results confirmed that the effectiveness of the proposed strategy to control hybrid power storage in coordination with the wind generator and the frequency recovery process is improved. Also, employing the optimum values guaranteed the frequency damping effectively with low operation cost. The Integral Absolute Error (IAE) value and operation cost are reduced by 13.6&#x0025; and 32&#x0025;, respectively. Also, the simulation results show that the maximum MG frequency deviation and maximum <italic>df/dt</italic> is well compatible with different standards in the presence of load perturbations and different wind speeds.

Plug-in hybrid electric vehicle (PHEV) has become a significant component in the fundamental infrastructure of microgrids (MGs). It is therefore of crucial practical interest to coordinate PHEVs with the rest of the grid. Supply demand coordination is an essential way to attain high energy efficiency at the system level while satisfying the stability of the grid requirement. This problem is challenging due to the high uncertainty from both the demand and supply sides in the system. This paper proposes a PHEVs smart charging that can achieve optimal operation for MGs. The proposed power management strategy is designed to minimize the MG dependency on the main grid, and to charge PHEVs based on maximum employment of RERs. Two scenarios are considered: the first scenario is designed so that PHEVs are charged without optimization and, the second case where PHEVs is charged in a smart way with optimization in order to reduce MG dependency on the main grid. Modified IEEE 33-bus system is simulated by MATLAB to verify the performance of the proposed power management strategy. The off-line digital time-domain simulations and software verification show that energy exchange between MG and the main grid is reduced. Also, the RERs generation is increased in order to charge the PHEVs.

Microgrids have become valuable assets because they improve the reliability of consumers while integrating renewables via distributed energy resources (DERs). Thus, making them cost-efficient is essential to secure their proliferation. This paper proposes a new method for the optimal design of microgrids. The proposed two-stage method optimizes the size and the location of the DERs, i.e., the renewable energy sources (RESs), distributed generation (DG) units, and battery energy storage systems (BESSs). Furthermore, the overall operation of the microgrid is optimized using a stochastic scenario-based approach, considering grid-connected and unintentional islanded modes. The proposed method also considers internal network reinforcements. Thus, the first stage is an energy-based approach, formulated as a mixed-integer linear programming (MILP) problem, and it is used to size the DERs, whereas the second stage uses an optimal AC power flow (AC-OPF) to formulate a mixed-integer nonlinear programming (MINLP) model that allocates the DERs and selects the best conductor for each circuit. The multi-objective nature of the problem is addressed via Pareto optimization to analyze the trade-off between operational and capital costs. The MINLP model is linearized through piece-wise approximations and solved using commercial solvers. Furthermore, the impact of battery degradation is analyzed through a simple adaptation of the Stage 1 model. Results were obtained with data from the real university campus microgrid CampusGrid, located at the State University of Campinas (UNICAMP), in São Paulo, Brazil.

Optimal techno-economic design of hybrid PV/wind system comprising battery energy storage: Case study for a remote area

Irrigation is an essential part of agriculture which helps to sustain crop growth and increase food productivity. Most of the nations around the globe have adopted diesel fuel-based pumping units to irrigate their farm lands. However, increased fuel cost and strict emission laws have made these nations to look for alternate and clean energy powered pumping units. Solar water pumping units are more promising alternate to address these concerns. In this review work, types and concepts of available solar thermal and electric energy-based water pumping units are discussed. Suitability of solar PV pumping units in comparison to thermal energy-based units has been listed out. Detailed procedure for sizing solar PV pumping units by considering crop water requirement, head of pump, and local climatic conditions like solar radiation intensity and rainfall have been provided based on inputs from available literatures. In addition, step by step procedure to estimate economics and environmental impacts associated with solar PV water pumping units along with results of latest studies in these areas have also been presented. Solar PV water pumping units are highly recommended for regions with at least 300 to 400 mm rainfall per year and 2 km away from local grid power supply. Moreover, operation of solar PV water pumping units in on-grid mode can reduce its payback period significantly. Pumping cost associated with diesel units are 300.0% higher than solar PV units. Hence, solar PV water pumping units can be considered as an effective and sustainable option to irrigate farmlands. Advantages, limitations of solar PV water pumping, and strategies to improve its acceptability among farmers have also been provided.

The importance of using renewable energy systems (RESs) worldwide has been consolidated. Moreover, connecting more RESs to the utility grid will lead to more technical problems. Photovoltaic (PV) and wind turbine (WT) based power plants are the most nonlinear sources of renewable energies contributing to the energy mix Electronic ballast and switching mode power supply in energy conservation of the PV and WT have caused power quality problems and less reliable output voltage. PV power plants are becoming increasingly integrated with the utility grid by onboarding certain superior power quality features. This grid integration drastically reduces the use of fossil fuels and prevents environmental hazards. This article presents the design of a 26 MWp grid-connected PV power plant, which is already tied to the Egyptian electrical network in Fares City, Kom Ombo Center, Aswan Governorate, Egypt The 26 MWp PV power plant consists of (11) blocks and the utility grid, which are simulated using Matlab/Simulink. Every block contains 2,376 kWp PV arrays connected directly to DC-DC boost converters to regulate the output DC power generated by each PV array. This output DC power is fed into a particular type of inverter called a “central inverter”, which converts it to AC power. In some cases, higher harmonic distortion at the grid and a greater negative impact on the power system performance occur when using this type of inverter. To optimize the gains of the proportional-integral (PI) controller for both the voltage and current regulators of this central inverter, meta-heuristic optimization techniques (MOTs) are used. During this article, Gray Wolf Optimization (GWO), Harris Hawks Optimization (HHO), and Arithmetic Optimization Algorithm (AOA) are applied as MOTs to enhance the quality of the power and voltage in addition to limiting the total harmonic distortions (THD) under the effect of different sunlight conditions and partial shading. As a result, the AOA-based controllers are found to show outstanding results and superior performance compared to GWO and HHO regarding solution quality and computational efficiency. Finally, MOTs are the best solution to most electrical problems regarding controlling nonlinear and high-penetration systems, such as PV power plants connected to the utility grid.

Flexible AC transmission systems (FACTS) and optimal power-flow (OPF) solutions play an important role in solving power operation problems. The volatile nature of the power generation profiles from renewable energy sources, solar and wind systems, and determining the optimal locations and sizes of FACTS devices increase the complexity of the OPF problems in modern power network models, such as transmission power loss, power generation operation cost and voltage deviation, as a highly nonlinear-nonconvex optimization problem. Therefore, this article introduces and employs four new independent, reliable and efficient optimization algorithms inspired by nature and biological nature, namely: Slime Mould Algorithm (SMA), Artificial Ecosystem-based Optimization (AEO), Marine Predators Algorithm (MPA) and Jellyfish Search (JS), for solving both multi- and single-OPF objective problems for a power network incorporating FACTS and stochastic renewable energy sources. The proposed new metaheuristic optimization techniques are compared to the common and available alternatives in the literature, Particle Swarm Optimization (PSO), Moth Flame Optimization (MFO) and Grey Wolf Optimizer (GWO), using IEEE 30-bus test system. To consider and address the challenges of the OPF in modern power network models, the proposed optimization techniques tested under different operation cases such as an increasing in the load, with and without FCTAS and renewable energy sources, different renewable energy sources locations on the network. The result showed that the MPA, SMA, JS and AEO algorithms are more effective solvers for the OPF problems cases compared to the PSO, GWO and MFO algorithms. For example, the AEO obtained 0.0844 p.u. in case of minimizing the voltage deviation compared to 0.1155 p.u. for PSO, which means that the AEO algorithm improved the voltage deviation term by 27% compared to the PSO algorithm.

Microgrid dynamic combined power–heat economic-emission dispatch with deferrable loads and price-based energy storage elements and power exchange

This paper presents a multi-objective optimization approach for optimal Distributed Generation (DG) placement and sizing, optimizing power loss reduction, cost efficiency, voltage stability, and Renewable Energy Source (RES) absorption. The Gray Wolf Optimizer (GWO) was chosen for its strong global search, fast convergence, and ability to avoid local optima. Simulations on IEEE 33-bus and IEEE 69-bus systems compared GWO against the Cuckoo Search Algorithm (CSA), Multi-Objective Particle Swarm Optimization (MOPSO), and Genetic Algorithm (GA). The results showed that GWO achieved the least power loss and highest RES absorption, enhancing efficiency, stability, and sustainability. This study demonstrates the effectiveness of nature-inspired optimization in DG planning and RES integration.

In this paper, we present an event-driven automatic demand response (EADR) framework for online operation of residential microgrids (RMGs). Our framework involves comprehensive analysis of the schedulable ability (SA) for residential energy resources (RERs), and the uncertainties of both sides are also considered. Specifically, we first construct an EADR architecture to minimize the total operation cost and maintain supply–demand balance, and the event analyses are provided. Then, we define the concept of SA for RERs and also establish an SA evaluation system taking the real-time and historical information of the RERs into account. On these bases, SA-based interactions between RERs and EADR system and state machine are introduced to trigger the execution of the online EADR in a close-loop way. Furthermore, to implement this framework in practice, we functionally decompose the overall EADR architecture into event manager, EADR server, and a group of local controllers, and also design distributed online algorithms for each of them. The key idea of the designed algorithms is to operate the RMG simulating its real-life dynamics. Numerical simulations on a practical RMG verify the effectiveness of the proposed approach.

Due to emerging environmental problems from fossil fuel usage and increasing renewable energy resources in microgrids, the need of demand response (DR) is intensified to stabilize irregular balance and to mitigate carbon dioxide emissions simultaneously. However, in order to estimate practical and factual DR potential, an analysis of load characteristics including user behavior is required based on information data. Thus, in this study, a novel analytic approach is proposed to detect resident behavior for environment responsive DR potential estimation. A new framework to determine optimal DR potential is proposed with related data analysis models. In each analysis model, resident occupancy behavior and forecasted renewable energy generation are estimated. During the process of resident behavior detection, sub-metering data of appliances are analyzed and profiled for multiple purposes by a new method based on hidden Markov model and time varying Markov chain. The optimal DR potential is estimated considering guaranteed residents' comfort constraints and the dynamic characteristics of appliances. The proposed framework is tested on the single household environment of residential region and the results present that the proposed model is environmentally and economically effective, while also suggesting meaningful implications through resident behavior data analysis.

Smart micro-grids offer a new and wide application domain for power electronics. In fact, every distributed energy resource (DER) includes an electronic power processor (EPP) to control the power exchange with the grid. If such distributed EPPs perform cooperatively, they have the capability to fully exploit all renewable power sources and energy storage units while improving the power quality and transmission efficiency. In fact, EPPs control phase and shape of the currents injected into the grid, thus they can provide, in addition to dynamic control of the power flow between DERs and grid, also voltage stabilization and harmonic damping. This is of particular relevance in low-voltage residential micro-grids, where number of active DERs and generated power are unpredictable and may vary during the daytime. A simple and effective solution to achieve cooperative operation of EPPs is described in the paper. It is based on a control approach which requires communication between neighbor units only (surround control) and holds even in presence of multiple feeding points from the utility. Moreover, it allows independent and stable operation of the EPPs while providing voltage support and reduction of distribution losses. Accordingly, residential micro-grids may perform efficiently, adapt to supply and load variations and switch automatically from grid-connected to islanded operation.

Optimal economic scheduling of microgrids with photovoltaic (PV) and wind generation has gained increased attention during recent years. Integration of renewable energy resources in microgrids requires increasingly active control and management of energy storages and demand response (DR). In this paper, a risk-based stochastic optimal energy management model is developed for microgrid with renewables, energy storage and load control by time-of-use-based DR programs. Microgrid includes PV system, wind system, micro-turbine, fuel cell, electric vehicle (EV), and energy storage. Information-gap decision theory (IGDT) is employed to address the uncertainty of loads and to provide the operating strategies for the microgrid controllable energy resources. This proposed model has been solved as a mixed-integer non-linear programming (MINLP) in General Algebraic Modeling System (GAMS) software and simulation results in different conditions are studied and discussed. Three different risk management strategies have been studied such as risk-averse, risk-neutral and risk-seeker mode. The simulation results indicate that the impacts of risk-averseness or risk-seeker of the decision maker affect the system operation. For instance, the results showed the DR program's role in risk-averse and risk-taking strategies, impacting consumption and costs. The proposed model ensures the risk-averse decision-maker that if the uncertain parameter deviates within the optimum robustness region, the final cost will not exceed the critical cost. On the other hand, the risk-seeking decision-maker can reach lower final costs by accepting the risks if the uncertain parameter deviates favorably within the opportunity region. Decision-makers can manage risks by adjusting consumption. Thus, considering the cost of risk management is crucial, as it increases with robust or opportunistic approaches.