Microgrid Planning Research Articles

Energy management and operational planning of renewable energy resources-based microgrid with energy saving

• Proposing an environmental/economic model for optimal energy management. • Considering renewable energy resources in the presence of Photovoltaic and battery. • Presenting Hybrid Whale and Pattern Search Algorithm for optimizing Micro-grid. • Reporting superior solutions in Short-Term Scheduling and Micro-grid Energy Management. Energy resource scheduling is one of the major problems in power systems. This issue has become even more significant as renewable sources with intermittent power output are becoming more and more prevalent. Due to their low environmental impact and low operating costs, renewable energy sources (RESs) have attracted interest. The excess power generated by such generation methods may be a negative that affects power systems, hence issues relating to such systems should be properly handled. The optimal operation of a microgrid (MG) with several distributed generation (DG) units and uncertain behavior of RESs is suggested in this research using a stochastic optimization approach. So, for an MG fitted with a solar photovoltaic (PV) unit, this research proposes a day-ahead scheduling paradigm. In this regard, the effects of various climatic circumstances on the power production of the PV unit and the optimal scheduling of the MG have been examined in this research. In order to achieve this, statistics on solar irradiance were extracted from four distinct days from each of the four seasons. The single-objective optimization framework used to design the scheduling problem states that the objective function should be to minimize the total operating cost across the scheduling period. The aforementioned day-ahead scheduling problem can be solved by the "hybrid whale optimization algorithm and pattern search (HWOA-PS)” optimization algorithm, while both renewable and nonrenewable generating units, as well as an energy storage system are present. In order to confirm the higher performance of the recommended approach, a thorough comparison between the Hybrid WOA-PS algorithm and a few well-known optimization algorithms has also been conducted.

Multi-period design of hybrid microgrid under multi-timescale uncertainties

This study focuses on the deployment planning of hybrid microgrid that uses energy storage and power-to-hydrogen (P2H) systems for combatting the variability and intermittency problem of renewable energy sources (RES). To consider the long-term uncertainties, such as those related to demand growth and installation costs, the deployment problem is formulated as a multi-period stochastic decision problem. Given that deployment decisions are intimately linked with how the system is operated against the short-term variabilities of the energy generated from the RES, the overall decision problem is multi-scale in nature, and computation for solving the problem can quickly become exorbitant. To address this complexity, this study proposes a combination of reinforcement learning and mathematical programming in a complementary, hierarchical way. The proposed method is demonstrated through a case study, where deployment decisions over multiple decades are considered along with hourly operational decisions within each period under various uncertainty types. The deployment plan obtained using the suggested method shows an 11% improvement in the economic performance compared to the two-stage stochastic programming (2SSP) approach.

A Data-driven Robust Model for Day-ahead Operation Planning of Microgrids Considering Distributed Energy Resources and Demand Response

HIGHLIGHTS Novel data-driven approach to uncertainties in microgrid resources Faster convergence that stochastic optimization Reduced conservativeness compared to robust optimization Microgrid system with comprehensive selection of distributed energy resources

Microgrids Multiobjective Design Optimization for Critical Loads

Since microgrids with renewable generation and energy storage can achieve high reliability, they present an attractive solution for powering critical loads. Microgrids should be carefully planned and optimized to meet the power requirements of critical loads and justify their economic viability. Conventional microgrid design approaches consider a fixed power architecture, focusing mainly on improving the financial aspects of the design by sizing its energy sources. This paper introduces a new Vectorial Microgrid Optimization (VMO) design method for critical loads. The proposed VMO improves the microgrid design by 1) incorporating the selection of the microgrid power conversion architecture and the size of the energy sources into a unified design strategy, 2) implementing multi-objective optimization to find the desired balance between the microgrid power supply availability, net present cost, and power efficiency. Through the use of graph theory, the proposed VMO automates the architecture selection process. The benefits of the proposed VMO method are highlighted in a critical load case-study to find the optimal microgrid design. The obtained microgrid design is compared with the results of existing microgrid planning tools and the standard industry approach for powering critical loads.

A Review on Challenges in DC Microgrid Planning and Implementation

A Review on Challenges in DC Microgrid Planning and Implementation

Probabilistic optimization of planning and operation of networked microgrids with renewable energy resources considering demand response programs

Probabilistic optimization of planning and operation of networked microgrids with renewable energy resources considering demand response programs

Capacity planning of carbon-free microgrid with hydrogen storage considering robust short-term off-grid operation

Carbon-free microgrids (CFMs) composed of renewable energy and hydrogen energy storage systems (HESS) are a promising solution for low-carbon energy systems. However, when CFMs are off-grid, their reliability is frequently questioned, especially when renewable energy (RE) outputs are extremely low. To reduce the total cost and improve the off-grid reliability, this paper proposes a two-stage robust planning model for CFM with renewable energy and HESS that considers both grid-connected (GC) and short-term (several days) off-grid (OG) statuses. In the GC status, the target is to minimize the system expenses; while in the OG status, the optimization goal is not to lose the important loads in a short period of time considering the worst realization of days. The Nested-C&CG algorithm is employed to solve the robust planning model of CFM. Additionally, a new uncertainty set that takes terrible days into account for RE and loads is presented to make the decision robust. Finally, the numerical analysis based on the case studies shows that the HESS in CFM has the ability to support loads in OG status for a week. And the total cost of the CFM using HESS and batteries can be reduced by 11.83% compared with that using only batteries. In addition, the planning results with robust optimization can guarantee that all the important loads are satisfied.

A Novel Design, Economic Assessment, and Fuzzy-Based Technical Validation of an Islanded Microgrid: A Case Study on Load Model of Kibber Village in Himachal Pradesh

Rural and remote area electrification is of grave concern around the globe. Therefore, well-planned and cost-effective microgrids integrating renewable energy sources are emerging as effective solutions. However, the microgrid's stable operation and its future deployment is affected by the perturbations caused due to uncertainity in renewable sources, dependency on the battery state of charge, and load variation. So, considering the possible concerns affecting the planning and development of a microgrid for any given region, this paper proposes a comprehensive performance assessment of the hybrid residential microgrid based on a load model of Kibber village in Himachal Pradesh, India. The proposed approach is divided into three parts for the best planning of microgrids. Firstly, the MATLAB–Simulink software technically analyzes the system performance under perturbations considering the available renewable sources. Secondly, an economic analysis using HOMER Pro software is done to examine the cost-effectiveness of the proposed microgrid model through the simulation of electrical loads for Kibber village, considering the available renewable sources. Lastly, a real-time analysis of the proposed prototype of programmable logic controller-based hardware test bench has been developed, aiming for future regional microgrid deployment. System voltage, frequency, power shared, percentage of load met, energy cost, available renewable energy resource, etc. have been considered for validating the proposed controller. The proposed comprehensive assessment of the microgrid model is reproducible with necessary modifications for any geographical location. It will be helpful for its future deployment aiming at rural and remote electrification.

Resilience Enhancement of an Urban Microgrid during Off-Grid Mode Operation Using Critical Load Indicators

Microgrids (MGs) can be used as a solution to ensure resilience against power supply failures in electricity grids caused by extreme weather conditions, unavailability of generation capacities, and problems with transmission components. The literature is rich in research focusing on strengthening the planning of microgrids based on overall load demand. In this study, a critical load demand indicator will be calculated and used to identify optimum operation strategies of microgrids in a power failure mode. An urban microgrid with a large educational building is selected for the case study. Operation dispatch scenarios are developed to reinforce the system’s resiliency in severe conditions. A mixed-integer linear programming (MILP) approach is employed to identify global optimum dispatch solutions based on a next 48 h plan for different seasons to formulate a whole-year operational model. The results show that the loss of power supply probability (LPSP), as an indicator of resiliency, could be lowered to near zero while minimizing operational cost.

A Noncooperative Game-Based Approach for Microgrid Planning Considering Existing Interconnected and Clustered Microgrids on an Island

This paper proposes a noncooperative game-based planning method for microgrid (MG) considering the interconnection between developing and developed privately-owned clustered MGs on an island. A noncooperative game with imperfect information is designed to regulate the decision-making in planning and operating strategies for rational MG owners. The optimization models for both developing and developed MGs are presented. Different levels of maturity of MG development are modeled by diverse objective functions and constraints, while the data privacy of privately-owned MGs is preserved through imperfect information and prior beliefs. To tackle the seasonality of renewable energy and load on an island, hydrogen storage is considered in the developing MG on the island. Diagonalization and realization are incorporated to develop an effective solving methodology in searching the mixed strategy equilibrium for each MG. The effectiveness of the proposed method is verified using comprehensive case studies.

Hydrogen-Based Networked Microgrids Planning Through Two-Stage Stochastic Programming With Mixed-Integer Conic Recourse

Networked microgrids that integrate the hydrogen fueling stations (HFSs) with the on-site renewable energy sources (RES), power-to-hydrogen (P2H) facilities, and hydrogen storage could help decarbonize the energy and transportation sectors. In this paper, to support the hydrogen-based networked microgrids planning subject to multiple uncertainties (e.g., RES generation, electric loads, and the refueling demands of hydrogen vehicles), we propose a two-stage stochastic formulation with mixed integer conic program (MICP) recourse decisions. Our formulation involves the holistic investment and operation modeling to optimally site and configure the microgrids with HFSs. The MICP problems appearing in the second-stage capture the nonlinear power flow of networked microgrids system with binary decisions on storage charging/discharging status and energy transactions (including the trading of electricity, hydrogen, and carbon credits to recover the capital expenditures). To handle the computational challenges associated with the stochastic program with MICP recourse, an augmented Benders decomposition algorithm (ABD) is developed. Numerical studies on 33- and 47-bus exemplary networks demonstrate the economics viability of electricity-hydrogen coordination on microgrids level, as well as the benefits of stochastic modeling. Also, our augmented algorithm significantly outperforms existing methods, e.g., the progressive hedging algorithm (PHA) and the direct use of a professional MIP solver, which has largely improved the solution quality and reduced the computation time by orders of magnitude. Note to Practitioners—This paper proposes an optimal planning model for electricity-hydrogen microgrids with the renewable hydrogen production, storage, and refueling infrastructures. Our planning model is extended under a two-stage stochastic framework to address the multi-energy-sector uncertainties, e.g., RES generation, electric loads, and the refueling demands of hydrogen vehicles. The first-stage problem is to optimize the siting and sizing plan of microgrids. Then, in the second-stage problem, the coordinated scheduling of electricity and hydrogen supply systems is modeled as second-order conic programs (SOCPs) to accurately capture the power flow representation under stochastic scenarios. Also, the logical constraints with binary variables are introduced to describe the energy transactions and storage operations, which results in an MICP recourse structure. Note that the stochastic MICP formulation could be very challenging to compute even with a moderate number of scenarios. One challenge certainly comes from integer variables that cause the problem nonconvex. Another challenge follows from the fact that the strong duality of SOCPs might not hold in general. To mitigate those two challenges, we prove that the continuous relaxation of our recourse problem has strong duality, and make use of that continuous relaxation and other enhancements to design an augmented decomposition algorithm. As revealed by our numerical tests, the proposed decomposition method outperforms PHA in both the solution quality and computational efficiency. Comparing to the PHA, our ABD method often achieves tighter bounds with trivial optimality gaps. Also, it could reduce the computation time by orders of magnitude. With the help of advanced analytical tool, the proposed planning framework can be readily implemented in real-world applications.

Deep Learning Approaches for Long-Term Global Horizontal Irradiance Forecasting for Microgrids Planning

Providing sustainable energy to rural communities is considered in Sustainable Development Goal 7. Off-grid renewable energy systems arise as an affordable solution due to their portability and the availability of renewable sources for rural communities. In this work, to deal with the uncertainties of solar resources, we employ two deep learning models (feed forward and recurrent neural networks) to predict renewable sources in a long-term horizon. To this aim, the approach presented takes into account the necessity of a high enough resolution in the forecasting output. As a case study, we employ open source data for a location in Michoacan, Mexico as well as open source programming frameworks to ensure the replicability of the numerical experiments. The results show that our prediction model performs excellently with respect to the baseline methods (ARIMA, exponential smoothing, and seasonal naive) in terms of the evaluation metrics MASE (18.5% of reduction with respect to seasonal naive), RMSE (24.7%), WAPE (13.1%), MAE (12.9%), and APB (8.9%).

Uncertainty modeling methods for risk-averse planning and operation of stand-alone renewable energy-based microgrids

The accuracy of models to capture the uncertainty of renewables significantly affects the planning and operation of renewable energy-based stand-alone (REB-SA) microgrids. This paper aims to first study different stochastic and deterministic models for renewables, then evaluate the performance of an REB-SA microgrid planning problem and provide qualitative and quantitative comparisons. A modified Metropolis-coupled Markov chain Monte Carlo simulation is considered for the first time in the planning of an REB-SA microgrid to predict the behavior of renewables with minimum iterations. The modified model is benchmarked against two prevalent models including the retrospective model with worst-case scenarios and the Monte Carlo simulation. The operations of three designed microgrids (by these three methods) are evaluated using the last three-year historical data of a city in northern Sweden including solar radiation, wind speed, the water flow of a river, and load consumption. The impacts of the considered methods on using PV panels and hydrogen systems are investigated. The results verify that the modified model decreases the risk of planning and operation of an REB-SA microgrid from the energy and power shortage viewpoints. Moreover, the designed microgrid with the modified model can cope with all possible scenarios from economic, technical, and environmental viewpoints.

Real-Time Electromagnetic Transient Simulation of AC-DC Networks [Book News

This book provides fundamental methods and hands-on case studies so that readers can become familiar with invaluable modeling methodologies for real-time emulation of modern electrical networks. It is suited for librarians, simulation specialists, advanced modeling and simulation engineers, microgrid planning, and designing professionals and professors in related fields, providing concepts to further explore new trends in real-time modeling and HIL. The book is also essential for engineers/ researchers willing to launch spinoffs in real-time modeling and HIL to take advantage of recent and future developments in FPGA technology.

A genetic algorithm optimization approach for smart energy management of microgrids

Optimal management and planning of microgrids (MG) are the most important goals for operators. In this study, a Multiobjective Genetic Algorithm (MOGA) is applied to the technical and economic problems of the MG. This stochastic programming considers demand response (DR) programs, reactive loads, and uncertainties due to renewable energies. Demand-side management (DSM) is how to manage and schedule the generation and consumption with the objective of cost and greenhouse gases (GHG) emissions minimization. In this work, with the contribution of various customers to demand response programs and reserve schedules, a reduction in the operation cost of the microgrid has resulted. This method facilitates obtaining a complete and comprehensive microgrid model for energy management in the power system, and the results demonstrate that participation in demand response programs and reactive loads can reduce generation, reservation, startup costs, and the amount of pollution. Regarding reservation costs, a 16% reduction was obtained in the presence of the load response, and wind power is a good compromise between cost and pollution among various resources.

Robust optimal capacity planning of grid-connected microgrid considering energy management under multi-dimensional uncertainties

Microgrid is considered an efficient paradigm for managing the massive number of distributed renewable generation and storage facilities. The optimal microgrid capacity planning is a non-trivial task due to the impact of randomness and uncertainties of renewable generation sources, and the adopted energy management strategies. In this paper, an optimal capacity planning model for the grid-connected microgrid is developed fully considering the renewable generation uncertainties through efficient scenario generation and reduction based on the deep convolutional generative adversarial network (DCGAN) and improved k-medoids clustering algorithm, as well as the microgrid energy management strategy. The proposed solution optimizes the capacity planning for the maximization of renewable energy utilization efficiency, and minimizes the economic cost and carbon emissions. The proposed solution is assessed using a case study of a microgrid (MG) project in northern China through a comparative study and the numerical results confirm the cost-effectiveness of the proposed solution.

Shapley Value Method and Stochastic Dantzig–Wolfe Decomposition for Decentralized Scheduling of Multimicrogrid

The decentralized economic scheduling of multimicrogrid is an important aspect in the operational planning of microgrids (MGs). This article proposes an approach to maximize economic benefit among MGs through cooperative scheduling. The cooperative scheduling is achieved via price signals so that MGs are encouraged to share power among themselves for economic benefit. An MG operator generates a time-variable tariff based on energy trading status so that the parking lot operator and distributed battery energy storage system aggregator participate with flexibility in the MG’s energy management. The Shapley value method is used for generating fair price signals. The stochastic Dantzig–Wolfe decomposition is used to solve the resulting optimization problem in a decentralized manner. The uncertainties related to load demand and renewable energy sources are captured using scenario-based methods, whereas the uncertainty associated with plug-in hybrid electric vehicles is modeled using copula theory based estimation. The simulation studies and comparison with the existing methods establish that the proposed approach effectively reduces the total energy cost in a decentralized manner with the minimum amount of information exchange.

Planning and optimization of microgrid for rural electrification with integration of renewable energy resources

Microgrids are an effective means to provide power to urban and rural communities. Microgrid planning must anticipate both the system's economic feasibility and long-term stability. Due to existing challenging ambitions, limitations, and the uncertainty of renewable energy production, the planning of microgrids is a difficult task. In the present work, a standalone microgrid is planned to integrate solar, wind turbine, diesel generator, and battery for the rural community of the hilly state of Uttarakhand (India). The Feasibility and techno-economic analysis of a proposed microgrid is conducted. The microgrid's performance is investigated using differential evolution and other existing algorithms, particle swarm optimization, and genetic algorithm to ascertain the cheapest option to energize the area. The sizing of components and sensitivity analysis of energy cost with changes in various parameters of the energy system is investigated. The optimization results suggest that the energy cost is $0.16/kWh, with a renewable fraction of 92% and a net present cost of $ 41,078. The obtained results indicated that a renewable-based energy system provides a more effective solution for the electrification of the rural population.

Economic and optimal planning of a multi-carrier microgrid consisting of electricity to gas conversion system and storage with developed algorithm of championship in sports leagues

The rapid development of power-to-gas (P2G) technology has led to promote the renewable energy integration and to transform the excess renewable production to combined normal gas at appropriate time. As the demand for power system increases, further recovering to excess wind energy by P2G is meat increase requests for energy and decrease system costs. In order to grow P2G transformation performance, in this work the MES model modeling to CO2-based operations and P2G are presented with the developed sports league championship algorithm. Mentioned method is considred the heat resumption for process of converting to hydrogen potency. The CO2-based ETES is presented to heat resumption of electrolysis procedure, that acts as a CO2 supplier in the methanation reaction. The championship algorithm in the sports league is equipped with teams with a network structure to extract rules multiple times. Rules are extracted multiple times by an algorithm in which each rule, in addition to current information, also contains information from the past. Therefor, in this study a memory is created to store useful information in each of the rules. In this study, the case studies show that the presented model can achieve more than 70% of the efficiency of excess wind energy recovery.

Optimal planning of green microgrid with minimisation of environmental emissions

In this article, optimal usage of renewable energy sources employed in an Indian microgrid network located in a rural district area of Orissa state, i.e. Sundargarh, is presented. Unlike the existing literature on microgrid development strategies for the Indian microgrid scenario, in this study, the known renewable energy fraction is being utilised to determine an optimal solution for the usage of different variants of possible renewable energy sources employed in a microgrid network. Note that while considering the operating cost, total net present cost, levelized cost of energy and environmental emissions herein the optimal size of wind, solar photovoltaic, hydro, diesel generator, power converter and battery are altogether considered for evaluating the performance of the microgrid network. Nevertheless, this study facilitates estimating the overall cost reduction in a heavily dispersed renewable energy-based microgrid network.