Optimal Operation Of Microgrid Research Articles

Optimal operation of multi-energy microgrids with shared energy storage clusters by master-slave games

Abstract As shared energy storage power plants have become a research hotspot, the cooperation between them and microgrids has attracted attention. In this paper, we propose a master-slave game optimization operation strategy for multi-energy microgrids, taking into account shared energy storage (ESS) clusters. Firstly, the inclusion of ESS clusters is considered in a combined heat and power (CHP) type multi-energy microgrid. Then, the optimal operation model is established and solved in two layers. The master-slave game model in the context of ESS clusters is proposed by analyzing the game relationship between the microgrid side and the user side, the arithmetic simulation with MATLAB, and the modeling and solving are carried out by the Yalmip tool and the CPLEX solver, and the combination of heuristic algorithms and the solver is used to optimize the strategies of the microgrid side and the user side. The optimization of the system after the optimization of this paper’s methodology can enhance the system economy.

Parametric Dueling DQN- and DDPG-Based Approach for Optimal Operation of Microgrids

This study is aimed at addressing the problem of optimizing microgrid operations to improve local renewable energy consumption and ensure the stability of multi-energy systems. Microgrids are localized power systems that integrate distributed energy sources, storage, and controllable loads to enhance energy efficiency and reliability. The proposed approach introduces a novel microgrid optimization method that leverages the parameterized Dueling Deep Q-Network (Dueling DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms. The method employs a parametric hybrid action-space reinforcement learning technique, where the DDPG is utilized to convert discrete actions into continuous action values corresponding to each discrete action, while the Dueling DQN uses the current observation states and these continuous action values to predict the discrete actions that maximize Q-values. This integrated strategy is designed to tackle the co-scheduling challenge in microgrids, enabling them to dynamically select the most favorable control strategies based on their specific states and the actions of other intelligent entities. The ultimate objective is to minimize the overall operational costs of microgrids while ensuring the efficient local consumption of renewable energy and maintaining the stability of multi-energy systems. Simulation experiments were conducted to validate the efficacy and superiority of the proposed method in achieving the optimal microgrid operation, showcasing its potential to improve service quality and reduce operational expenses. Average rewards increased by 30% and 15% compared to the use of the Dueling DQN or DDPG only.

Peer‐to‐peer electricity trading via a bi‐level optimization approach for renewable energy‐driven microgrids connected to the distribution grid

AbstractThis study employs a sophisticated bi‐level optimization methodology to model the most efficient operation of microgrids (MGs) within the operational framework of distribution companies (DCs). In this bi‐level optimization problem, the upper level strives to maximize the profits of both MGs owners and DCs, while the lower level is dedicated to ensuring load balance, managing distributed generation, and implementing load curtailment strategies. The coordination of power transmission is facilitated by the DCs. At the upper level of decision‐making, the optimal pricing strategies for power transactions are determined, accounting for various factors such as market prices, demand response programs, and uncertainties in wind speed. Through the utilization of a bi‐level optimization framework, this study comprehensively captures the complex interactions between MGs and DCs, taking into consideration the objectives and constraints of both entities. This approach offers a more precise representation of the decision‐making process in retail electricity markets, thereby providing valuable insights into the optimal operation of MGs within the DCs setting.

Dual layer energy management model for optimal operation of a community based microgrid considering electric vehicle penetration

This work develops a dual-layer energy management (DLEM) model for a microgrid (MG) consisting of a community, distributed energy resources (DERs), and a grid. It ensures the participation of all these energy entities of MG in the market and their interaction with each other. The first layer performs the scheduling operation of the community with the goal of minimizing its net-billing cost and sends the obtained schedule to the DER operator and grid. Further, the second layer formulates a power scheduling algorithm (PSA) to minimize the net-operating cost of DERs and takes into account the load demand requested by the community operator (COR). This PSA aims to achieve optimal operation of MG by considering solar PV power, requested demand, per unit grid energy prices, and state of charge of the battery energy storage system of the DER layer. Moreover, to study the impact of electric vehicles (EVs) load programs on DLEM, the advanced probabilistic EV load profile model is developed considering practical and uncertain events. The EV load is modelled for grid to vehicle mode, and a new mode of EV operation is introduced, i.e., vehicle to grid with EV demand response strategy (V2G_DRS) mode. The solar PV and load demand data are obtained from the MG setup installed and buildings present at the university campus. However, a scenario reduction technique is used to deal with the uncertainties of the obtained data. In order to evaluate the efficacy of the developed DLEM, its results are compared to previously reported energy management models. The results reveal that DLEM is superior to the existing models as it decreases the net-billing cost of COR by 13% and increases the profit of the DER operator by 17%. Further, it is found that for the highest EV penetration, i.e., 30 EVs, the V2G_DRS mode of EV operation reduces the total energy imported by COR by 11.39% and the net-billing cost of COR by 7.88%. Therefore, it can be concluded that the proposed model with the introduced V2G_DRS mode of EV makes the operation of all the entities of MG more economical and sustainable.

Optimal Operation of an Industrial Microgrid within a Renewable Energy Community: A Case Study of a Greentech Company

The integration of renewable energy sources in the European power system is one of the main goals set by the European Union. In order to ease this integration, in recent years, Renewable Energy Communities (RECs) have been introduced that aim to increase the exploitation of renewable energy at the local level. This paper presents an Energy Management System (EMS) for an industrial microgrid owned and operated by a greentech company located in the north of Italy. The company is a member of an REC. The microgrid is made of interconnected busbars, integrating photovoltaic power plants, a fleet of electric vehicles, including company cars and delivery trucks supporting Vehicle-to-Grid (V2G), dedicated charging stations, and a centralized battery energy storage system. The industrial site includes two warehouses, an office building, and a connection to the external medium-voltage network. The EMS is designed to optimize the operation of the microgrid and minimize the operating costs related to the sale and purchase of energy from the external network. Furthermore, as the company is a member of an REC, the EMS must try to follow a desired power exchange profile with the grid, suggested by the REC manager, with the purpose of maximizing the energy that is shared within the community and incentivized. The results demonstrate that, when minimizing only costs, local self-consumption is favored, leading to a Self-Sufficiency Rate (SSR) of 65.37%. On the other hand, when only the adherence to the REC manager’s desired power exchange profile is considered in the objective function, the SSR decreases to 56.43%, net operating costs increase, and the energy shared within the REC is maximized.

ANFIS-based power management and islanding detection utilizing permeation rate(γ) and relaxation parameter(ζ) for optimal operation of multiple grid-connected microgrids

Microgrid(s) integration into distribution networks and the growing use of Distributed Generation Systems necessitate the switch from traditional fossil fuel-based power generation methods to renewable resource-based ones. To achieve optimal power delivery, an adaptive method for solving power quality delivery problems posed by this transition is required. Unexpected islanding is an important power security concern that can lead to equipment damage, electrical risks, and decrease in power quality delivery. To address this problem, ANFIS-Based power management and islanding detection utilizing permeation rate(γ) and relaxation parameter(ζ) is proposed in this paper based on a rate of change of output voltage(ROCOV) of power sources at the point of common coupling in a 34-bus distribution network. Modeling of the distribution network, distributed generators, ANFIS training, and simulations was done using MATLAB/Simulink software. Results show that a 0.3 and 1.01 value indicates permeation rate of Microgrid(s) in a grid-tied mode whereas a 0 value indicates relaxation parameter of Microgrid(s) disconnected from the main distribution network. Islanding detection time of 0.02 sec was recorded when all Microgrids were disconnected during disturbances, and a 0.04 sec islanding detection time for disconnecting individual power sources at a time was recorded respectively. In general, islanding causes a system nominal voltage(400 V) deviation of 7 %. However, this was quickly restored without causing network voltage fluctuations due to the adaptive nature of ANFIS. Comprehensive simulations were used to validate the suggested method. The outcomes demonstrated that the proposed approach effectively distinguishes between islanding and non-islanding events and can quickly and accurately identify islanding as compared to other related works.

Stability Constrained Optimal Operation of Inverter-Dominant Microgrids: A Two Stage Robust Optimization Framework

Stability Constrained Optimal Operation of Inverter-Dominant Microgrids: A Two Stage Robust Optimization Framework

Optimal operation of an electricity-hydrogen DC microgrid with integrated demand response

Uncertainties introduced by the high penetration of renewable sources, plug-in-hybrid-electric vehicle load demand, and limited capacity of firm generation available in a DC microgrid make the energy scheduling task rather challenging. This paper proposes a decentralized energy management scheme with a real-time pricing-based demand response implementation for a DC microgrid, considering the sectoral coupling between electricity and hydrogen energy. The objectives of the scheduling strategy are to maximize the profit of the DC microgrid operator and reduce the cost of energy use by the consumers, considering the interaction and interdependence of the electrical and hydrogen systems with a detailed DC microgrid network model and associated network constraints. The DC microgrid operator schedules flexible resources under its control (power procurement from the upstream grid, microturbines, battery energy storage, hydrogen storage, electrolyzer and fuel cell) and sets real-time prices. The consumers set their consumption patterns according to the real-time price. The DC microgrid operator side flexibilities are coordinated with the consumer side flexibilities (thermostatically controlled load like air-conditioner and plug-in-hybrid electric vehicle) using the decentralized “Alternating Direction Method of Multipliers” approach. The probabilistic Copula theory models correlated input uncertainties. Simulation results on a six-bus DC microgrid test system reveal that the operating cost of the DC microgrid operator reduces by ∼11.06% while the energy use cost of consumers reduces by ∼4.80% using the proposed approach for the system under study.

Microgrid based on ICA and improved cuckoo search algorithm optimizing scheduling methods

Abstract The optimal operation of microgrids can provide a strong guarantee for the efficient utilization of distributed power and renewable energy. To solve the important problem of optimized operation of microgrids with a large proportion of distributed power sources, a microgrid optimal scheduling and operation method is proposed in the paper. This method first establishes a microgrid model containing distributed power sources and uses the ICA algorithm to screen out the key factors in the economy of the grid system. Then, taking the construction and operation cost of the microgrid as the objective function, it proposes to solve the model based on the ICA and the optimized cuckoo search algorithm. The method is able to effectively schedule the microgrid objective function based on the conditions, with the ability to quickly solve the scheduling problem and greatly reduce the system’s electricity cost. It can greatly increase the utilization rate of photovoltaic power generation. The method has a small amount of computation, small storage, and simple operation. An empirical study is carried out on the designed microgrid optimal scheduling scheme.

Distributed Generation and Demand Response Coordination for Optimal Operation of Microgrid Using Modified Elephant Herd Optimization

The optimal operation and coordination of distributed generation (DG) sources and demand response (DR) programs help to minimize the operating cost of microgrids along with other mutual benefits. The model is developed, and the multiobjective problem subjected to the set of constraints is solved by using the modified elephant herd optimization (MEHO) technique in this work. The expected output of the model is the optimal generation scheduling plan of both the dispatchable and non-dispatchable DG and optimized cost for the isolated microgrid operation. The other objectives related to the environmental aspects and reliability are also considered for optimization. DG operation is coordinated with the day ahead DR programs to minimize the costs while satisfying the operation limits of the DG sources and consumer demand. The novelty of the work lies in finding the optimal solution considering the multiobjective for optimal DG operation and analyzing the impact of different types of DR programs on microgrid operation by the MEHO technique. The effectiveness of the proposed model and validity of the algorithm is further tested on a 14 bus microgrid benchmark system. Through simulation and optimization results it is observed that the proposed algorithm has less computational time and better accuracy.

Chance-Constrained optimal scheduling of power and hydrogen systems in power distribution network with significant photovoltaic and carbon offsetting strategies

The urgent need to address climate issues increases the significance of sustainable strategies in power networks. This study focuses on the optimal operation of a hydrogen-based microgrid incorporated with photovoltaics (PVs), time-of-use demand response (TOU-DR) program, distributed generation (DG) units, hydrogen fuel stations (HFSs), and battery storage units. Employing a multi-objective approach, the study aims to minimize emissions and system operation costs, being validated by implementing it on the IEEE 33-bus test network. To address the carbon emissions issue, the Power-to-gas (P-to-G) facility is utilized to capture and recycle CO2 pollutions. The implementation of a carbon trading market mechanism is also adopted to further enhance the generation capacity of the PV units and facilitate the achievement of sustainable development goals. Besides, the presence of uncertainties in renewable generation, demand, operational characteristics of hydrogen vehicles, and market prices pose challenges for the system operator, which is addressed by employing the chance constraint programming (CCP) method. Results show that using the P-to-G system has led to a 10 % emissions reduction. However, this is accompanied by a corresponding 25 % increase in operational costs. Additionally, when operating under the CCP constraints, the system's storage level has been increased by 10 % to ensure optimal performance.

Performance analysis of machine learning based prediction models in assessing optimal operation of microgrid under uncertainty

Of late, the exponential rise in the global population is driving higher energy demand. However, the rapid depletion of conventional fossil fuels and growing environmental concerns have prompted the evolution of alternative energy sources. To this end, Microgrid (MG) with Renewable Energy Sources (RES) has emerged as popular means of small-scale localized power grid. However, planning of MG operation poses challenges due to the inherent variability and stochasticity in RES power output and energy demand. On account of this, the present study introduces a Stochastic Energy Management Strategy (SEMS) for a grid-connected MG incorporating Micro-Turbine, Fuel-Cell, RES, Battery Energy Storage, and electrical and heat energy demand. The stochasticity of RES is forecasted through a hybrid prediction model (sARIMA-GRU) and the uncertain demand is estimated via 'Monte Carlo Simulation.' The proposed problem is formulated as a dynamic non-linear stochastic optimization problem. It seeks to minimize the expected value of MG operational cost satisfying the practical constraints. Addressing this, a newly developed ‘Artificial Electric Field Algorithm (AEFA)' is utilized. Several case studies are performed to assess MG operation under varied operating conditions. Moreover, the present study analyses the impact of uncertainty on energy contribution from DER, grid dependency, and MG operation cost. Comparative analysis reveals that sARIMA-GRU outperforms other contemporary prediction models. It is noteworthy that the superior prediction accuracy of sARIMA-GRU leads to lower MG operation costs. Moreover, statistical analysis and convergence confirm the proficiency of applied AEFA over state-of-the-art Grey Wolf Optimization and Firefly Algorithm in solving the proposed problem.

Cryptocurrency mining as a novel virtual energy storage system in islanded and grid-connected microgrids

Renewable electrical energy (such as: solar and wind energies) generation in microgrids (MGs), is gaining attention to reduce greenhouse gas emissions. Microgrid operators (MOs) aim to create self-sufficient, environmentally sustainable grids, increasing the capacity of renewable energy sources (RESs) by up to 100%. Despite of the benefits of this trend, challenges arise from non-controlled characteristics of these power generations and their seasonal variations, causing fluctuations and renewable energy curtailment. Although the technical solutions; such as: the demand response (DR) programs, and the conventional electrical energy storage systems (EESSs) can help, however those may face limitations in countries with high seasonal energy generation and consumption variations. This paper introduces cryptocurrency mining loads (CMLs) as innovative virtual energy storage systems (VESSs), named cryptocurrency energy storage systems (CESSs). It proposes a structure to store excess renewable energy in cryptocurrency units (CCUs) like Bitcoin (BTC). CESSs can be charged during off-peak intervals and, conversely, they discharge during high-demand periods to reduce the overall operational cost of MGs. Furthermore, it presents a new energy management system (EMS) formulation for the optimal operation of MGs in the presence of CESSs, providing an opportunity to generate additional electricity from RESs and to mitigate renewable energy curtailment. This paper explores the optimal operation conditions of both islanded and grid-connected MG with the proposed CESS. Utilizing a dataset from an island in Finland as a practical MG, its effectiveness is demonstrated through several case studies. The results of one case study in this paper demonstrate that the proposed CESS can decrease the operating cost of the MG by about 46.5%. Additionally, it is showed that by application of CESS the renewable energy curtailment is significantly reduced, and approached zero.

Optimal sizing and energy management of a microgrid: A joint MILP approach for minimization of energy cost and carbon emission

The optimal design and operation of microgrids involves complex trade-offs between technical, economic, and environmental factors. This research addresses these challenges by proposing a comprehensive approach that combines the sizing and energy management problems of a microgrid into a single decision-making framework. The study introduces a joint multi-objective mixed-integer linear programming algorithm that minimizes the levelized cost of energy (LCOE) and life cycle emissions (LCE) of the microgrid formed by photovoltaic system, battery storage, and with/without grid connections. By considering 25-year project lifetime horizon and hourly energy management, the algorithm ensures an optimal solution that accounts for long-term planning and real-time operation. The research conducts an analysis of French electricity grid as a case study where a Pareto-front is created using the trade-off constraint approach. Furthermore, the impact of peak shaving is assessed on Pareto-front for various subscription power to utility grid. Lastly, the research examines energy sources in the French electricity grid, revealing insights into the energy mix’s implications for microgrid design. The results showed that LCOE and LCE can vary up to 28% and 17%, respectively, while the local renewable energy utilization can be increased to 54% by limiting the grid subscription power at 36 kVA.

A Three-Stage Optimal Operation Strategy of Interconnected Microgrids With Rule-Based Deep Deterministic Policy Gradient Algorithm.

The ever-increasing requirements of demand response dynamics, competition among different stakeholders, and information privacy protection intensify the challenge of the optimal operation of microgrids. To tackle the above problems, this article proposes a three-stage optimization strategy with a deep reinforcement learning (DRL)-based distributed privacy optimization. In the upper layer of the model, the rule-based deep deterministic policy gradient (DDPG) algorithm is proposed to optimize the load migration problem with demand response, which enhances dynamic characteristics with the interaction between electricity prices and consumer behavior. Due to the competition among different stakeholders and the information privacy requirement in the middle layer of the model, a potential game-based distributed privacy optimization algorithm is improved to seek Nash equilibriums (NEs) with encoded exchange information by a distributed privacy-preserving optimization algorithm, which can ensure the convergence as well as protect privacy information of each stakeholder. In the lower layer of the model of each stakeholder, economic cost and emission rate are both taken as operation objectives, and a gradient descent-based multiobjective optimization method is employed to approach this objective. The simulation results confirm that the proposed three-stage optimization strategy can be a viable and efficient way for the optimal operation of microgrids.

A Coordinated Optimal Operation of a Grid-Connected Wind-Solar Microgrid Incorporating Hybrid Energy Storage Management Systems

The hybrid-energy storage systems (ESSs) are promising eco-friendly power converter devices used in a wide range of applications. However, their insufficient lifespan is one of the key issues by hindering their large-scale commercial application. In order to extend the lifespan of the hybrid-ESSs, the cost functions proposed in this paper include the degradation of the hydrogen devices and the battery. Indeed, this paper aims to develop a sophisticated model predictive control strategy for a grid-connected wind and solar microgrid, which includes a hydrogen-ESS, a battery-ESS, and the interaction with external consumers, e.g., battery/fuel cell electric vehicles. The integrated system requires the management of its energy production in different forms, i.e., the electric and the hydrogen ones. The proposed strategy consists of the economical and operating costs of the hybrid-ESSs, the degradation issues, and the physical and dynamic constraints of the system. The mixed-logic dynamic framework is required to model the operating modes of the hybrid-ESSs and the switches between them. The effectiveness of the controller is analyzed by numerical simulations which are conducted using solar and wind generation profiles of solar panels and wind farms located in Abu Dhabi, United Arab Emirates. Such simulations, indeed, show that the proposed strategy appropriately manages the plant by fulfilling constraints and energy requests while reducing device costs and increasing battery life.

A hybrid butterfly algorithm in the optimal economic operation of microgrids.

With the increasing capacity of renewable energy generators, microgrid (MG) systems have experienced rapid development, and the optimal economic operation is one of the most important and challenging issues in the MG field. To reduce the overall generation cost of microgrids, a hybrid butterfly algorithm (HBOA) is proposed to address the optimal economic operation problem in MG systems. This algorithm uses adaptive switching thresholds to balance the global exploration capability and local exploitation capability of the algorithm. It introduces a diversity learning mechanism to enhance information exchange among populations to improve the algorithm's accuracy and proposes an elite-guided guidance strategy to accelerate the convergence speed of the algorithm. Numerical simulation experiments on 10 standard test functions validate that the HBOA algorithm has higher optimization accuracy and faster convergence speed. Simulation experiments are conducted on two operation modes of microgrids: Islanded and grid-connected, and compared with other algorithms. In islanded and grid-connected modes, HBOA can reduce operating costs by up to 11.7% and 17.7%, respectively. The experimental results confirm the applicability and superiority of the proposed algorithm for solving the optimal economic operation problem in microgrids.

Joint Optimal Sizing and Operation of Unbalanced Three-Phase AC Microgrids Using HOMER Pro and Mixed-Integer Linear Programming

Joint Optimal Sizing and Operation of Unbalanced Three-Phase AC Microgrids Using HOMER Pro and Mixed-Integer Linear Programming

Optimal sizing of battery energy storage systems and reliability analysis under diverse regulatory frameworks in microgrids

The integration of battery energy storage systems (BESS) with microgrids (MG) is crucial to improve the reliability and flexibility of renewable energy sources (RES) integration. However, the reliability and regulatory policies are critical factors that affect the optimal operation of MGs in the market. This study aims to enhance the reliability of MGs integrated with RES and BESS by evaluating their performance under different regulatory frameworks, namely feed-in tariff (FiT), net metering (NM), and energy storage incentive (ESI). Also, a dynamic FiT (D-FiT) framework is utilized to improve the reliability of the MG. An artificial bee colony optimization algorithm is utilized to optimize the size of BESS for each regulatory policy to minimize the total cost of the MG. Each policy is formulated based on its specific constraints in the problem. Subsequently, the reliability indices of Loss of Load Expectation (LOLE) and Expected Energy not Supplied (EENS) are calculated for each optimized solution. Moreover, we have integrated the dynamic thermal rating (DTR) system into our proposed model, focusing on the safe augmentation of system component ratings. The study finds that the D-FiT and standard FiT frameworks provide the best reliability level, whereas the reliability improvement under the ESI policy is not significant, as most of the MG's demand is supplied by the main grid. Furthermore, the study shows that the improvements in EENS are higher than LOLE, indicating that installing BESS reduces the loss of energy rather than the number of interruption hours. D-FiT framework has a significant positive impact on both reliability indices, unlike the other frameworks that have a greater effect on EENS. Furthermore, we have noticed a substantial improvement in reliability indices when the DTR system is taken into account, as compared to the static thermal rating (STR) system.

Cost-effective energy management of an islanded microgrid

The interest in the integration of distributed energy resources in microgrids increased significantly in the last decade. The stochastic nature of some energy sources and the dynamic power demand has brought difficulties regarding the optimal control of microgrids. This current study addresses the energy management challenge in an islanded hybrid energy microgrid that includes three types of renewable energy resources (photovoltaic, geothermal and biomass) and a battery storage system, using intelligent management methods that optimize energy production costs. The proposed intelligent management methods are based on cost function minimization. These ensure optimal energy management operation under uncertain weather conditions, keeping to the minimum the price of the energy produced by the microgrid. Due to the erratic nature of solar irradiance and considering that solar energy is cheaper than its counterparts (geothermal and biomass in this case), forecasting the photovoltaic power production is an essential task for guaranteeing optimal microgrid operation. Day-ahead photovoltaic power forecasts are issued based on solar irradiance forecasts provided by the Global Forecasting System and further integrated into the day-ahead scheduling of the microgrid. This algorithm, together with the cost-optimization algorithms, gives intelligence to the management protocol generating the input parameters (power and operating time for each generating unit) for the microgrid Energy Management Controller. Different cost optimization methods are studied and compared, including mathematical models, such as Mixed Integer Linear Programming, or nature-inspired optimization algorithms, such as Genetic Algorithm, Harmony Search Algorithm, and Particle Swarm Optimization. It is shown that these algorithms provided suitable solutions for the energy balance and scheduling ensuring minimal operation costs for the microgrid. The study also highlights the differences and limitations of these algorithms and shows a method on how these energy management methods can be integrated into a real-world microgrid providing a step forward in the development of intelligent microgrids.