Cost Of Microgrid Research Articles

A System Management Method for Photovoltaic Microgrid Based on Energy Circuit

Microgrid is an important part of smart grid, and the system management method of microgrid has been widely concerned. In this paper, a system modeling method based on energy circuit is proposed to model the microgrid system structure. The branches are represented by logic variables and state variables. The model of microgrid can be easily generated on the computer by inputting the parameters of nodes and branches. Then, a method of demand side response (DSR) based on probability is proposed to solve the problem of uncertain load estimation. On this basis, the DSR method is applied to energy management strategy (EMS), in which the return coefficients of loads are changed. The simulation results show that the modeling method is effective and feasible for the energy management system of microgrid. The EMS including demand side response can significantly reduce the start‐up time of diesel generator (DSL) and the operation cost of microgrid. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Microgrid System Evaluation Using Capacity Factor For an Off-grid Community in Nigeria

With the high rate of economic development, there is an increasing load demand even in rural communities and Obayantor in Nigeria. Obayantor, having zero connection to the primary grid, needs electricity. Following the community located in a tropical rejoin of Nigeria, it has enough solar energy sources. With the high price of diesel fuel, solar microgrid seems to be the best energy solution for the rural community. In this study, technical and economic analysis was carried out on the solar-based microgrid and compared with a diesel-only microgrid using MATLAB Software tool. The results show that the solar microgrid is more cost-effective and has a cheaper COE, affordable for the rural community compared with the diesel microgrid, which is more expensive to afford. The Annual system cost for the solar microgrid was lower compared with the diesel source. The result also revealed that the diesel-alone microgrid system is 4.71 times and 0.2 times more costly than the solar-based microgrid system in terms of the cost of energy and ASC, respectively. Thereby making the diesel microgrid not having technical and economic feasibility for the community.

Robust Scheduling of Networked Microgrids for Economics and Resilience Improvement

The benefits of networked microgrids in terms of economics and resilience are investigated and validated in this work. Considering the stochastic unintentional islanding conditions and conventional forecast errors of both renewable generation and loads, a two-stage adaptive robust optimization is proposed to minimize the total operating cost of networked microgrids in the worst scenario of the modeled uncertainties. By coordinating the dispatch of distributed energy resources (DERs) and responsive demand among networked microgrids, the total operating cost is minimized, which includes the start-up and shut-down cost of distributed generators (DGs), the operation and maintenance (O&M) cost of DGs, the cost of buying/selling power from/to the utility grid, the degradation cost of energy storage systems (ESSs), and the cost associated with load shedding. The proposed optimization is solved with the column and constraint generation (C&CG) algorithm. The results of case studies demonstrate the advantages of networked microgrids over independent microgrids in terms of reducing total operating cost and improving the resilience of power supply.

Optimization of emission cost and economic analysis for microgrid by considering a metaheuristic algorithm‐assisted dispatch model

AbstractElectric vehicles (EVs) have witnessed a steady and continuous rise in the last few years. The increased prevalence of EVs results in high demand for electricity from the power grid and it can be effectively handled by combining EV charging infrastructure with renewable energy sources (RES). However, the intermittent characteristic of RES introduces an additional challenge to the power grid. This paper proposes a metaheuristic algorithm called self‐adaptive elephant herd optimization algorithm (SA‐EHO) to achieve the desired dispatch model of microgrid (MG) with EVs and RES. The proposed algorithm optimizes both economic and emission cost of MG which comprises diesel engine (DE), solar photovoltaic (PV), EVs, fuel cell (FC), wind turbine (WT), and loads. The output constraints associated with a distributed power supply such as power limits of distributed generators (DG) and charging of EVs followed by its discharging are subjected to optimization. Finally, the performance of the proposed model is compared and proved over other existing models. Especially, a minimal total cost of the proposed model is 16.79%, 21.58%, 20%, 26.67%, and 1.33% superior to traditional HSA, GA, EHO, MSA, and FS‐MSA models at 100th iteration. Furthermore, the reserved cost of the proposed scheme for the third scenario is 77.78%, 90.91%, 85.71%, 90.91%, and 90.91% superior to existing models such as HSA, GA, EHO, MSA, and FS‐MSA, respectively.

Optimal stochastic scheduling in microgrids under managed risk

This paper addresses the stochastic optimal day-ahead microgrid (MG) energy resources scheduling, considering the uncertain load, price of electricity, and generated electrical power by wind and solar units. Moreover, the vehicle-to-grid (V2G) implementation, load curtailment cost, and spinning reserve requirements are modeled to make the results more practical and applicable. Furthermore, the price elasticity of supply is considered to explore the relation between V2G capability and the optimization process. The stochastic energy resources scheduling problem is formulated in a two-level optimization framework. The unit commitment of dispatchable resources is analyzed in the upper level, and the lower level is formulated as a scenario-based two-stage stochastic programming problem that minimizes the operation cost of MG considering all the constraints. The risk of attaining unfavorable high costs of MG scheduling is considered using the variance approach. The generated scenarios are reduced by using the backward reduction method for each uncertain variable at each hour, independently. The artificial intelligent-based methods, including differential evolution algorithm (DEA), particle swarm optimization (PSO), and covariance matrix adaptation evolution strategy (CMAES) are applied to solve the problem. The effectiveness of the proposed approaches is confirmed by simulations on a modified 13-bus IEEE test system, in two cases of neglecting the risk and including the managed risk by applying the real-world data. The results confirmed the better performance of CMAES for solving such optimization problems.

A Techno-Economic Analysis of Energy Storage Components of Microgrids for Improving Energy Management Strategies

Microgrids are essential elements of the energy transition because they allow optimal use of renewable energy sources (photovoltaic panels, wind turbines) and storage devices (batteries, supercapacitors) by connecting them to consumption poles (e.g., buildings, charging stations of electric vehicles). Lithium-ion batteries and supercapacitors are the main electrical storage devices usually used by microgrids for energy and power transient management. In the present paper, microgrid simulations have been performed. Electrothermal and aging models of storage components are presented. Strategies and scenarios for the batteries are presented either based on the state of charge limitation or hybrid association with supercapacitors. The contribution of this study is to provide a management strategy which considers the aging of storage systems in the real-time management of the microgrid in order to extend their life, while minimizing installation costs. The first approach for a techno-economic study provided in that study enables us to improve the strategies by optimizing the use of the battery. The results obtained in this paper demonstrate the key role of the techno-economic approach and knowledge of the aging processes of storage devices in improving the energy management and global feedback costs of microgrids. The simulation results show that battery life can be improved by 2.2 years. The improvement in battery life leads to a reduction in the total cost of the installation by reducing the cost of the batteries.

RETRACTED: Microgrid operation relying on economic problems considering renewable sources, storage system, and demand-side management using developed gray wolf optimization algorithm

Density of renewable energy sources in distribution systems has developed a novel structure, called microgrid (MG), which consists of small-scale power grids with controllable and uncontrollable loads. MGs are a combination of different distributed generation resources (DGRs) which act as a controllable system at the distribution voltage level and supply power or heat to a group of local loads. Due to the high fluctuations of available power at the distribution voltage level, MGs may fail to supply major consumers. Therefore, several MGs are used by dividing consumers into smaller units; each unit is supplied by one MG. In this study, renewable sources such as wind turbine (WT), photovoltaic (PV) cell, and hydrogen storage system are considered as MGs. A novel energy management strategy is proposed using hydrogen storage system and considering uncertainties of renewable sources in the MG. This strategy aims to minimize the operating costs of batteries and hydrogen storage systems as well as unsupplied and surplus energy costs considering load supply constraints. Various resource constraints are employed in the proposed strategy. Moreover, demand response (DR) program is applied for MG optimal operation. The proposed model is implemented on a system using MATLAB software and meta-heuristic gray wolf optimization (GWO) algorithm. The results indicate applying hydrogen storage system and demand-side management (DSM) program could reduce the final cost of MGs and the proposed method has high efficiency in solving complex problems.

Enhancement of battery life in microgrid energy management using mixed integer linear programming and hybrid knapsack

Microgrid with distributed energy resources and energy storage system provides sustainability and resiliency. In this research, residential community microgrid is examined with responsive loads that create flexible generation-demand model. An optimization algorithm using mixed integer linear programming (MILP) has been formulated to minimize the operating cost and emission of dispatchable power generation, with the help of demand response. Usually, in renewable energy–based grid-connected microgrid, the batteries are managed under partial state of charge (SoC) conditions due to the limit of power imported from grid. The proposed MILP model ensures full SoC operation and safe charging or discharging dynamics of the battery in order to enhance its lifespan. Moreover, the day-ahead scheduling of household appliances is carried out using a novel hybrid knapsack method, which combines binary and fractional knapsack algorithms. An electric vehicle battery is considered as a flexible power load, which offers an unique way of approach in scheduling of appliances. The results confirm that the power demanded by the appliances is fulfilled at the user-specified hour for maximum comfort along with minimum operating cost of microgrid. Generic algebraic modeling system (GAMS) tool is used to run the proposed algorithms.

Multi-time scale dynamic robust optimal scheduling of CCHP microgrid based on rolling optimization

Combined cooling, heating and power (CCHP) microgrid can effectively improve the efficiency of renewable energy and reduce the operating cost of microgrids. However, the uncertainties of renewable energy sources and variety load demands make the optimal scheduling of CCHP microgrid complex and difficult to achieve. In this paper, a novel multi-time scale dynamic robust optimal scheduling strategy is proposed for the coordinated operation of CCHP microgrid, which includes two time scales: the day-ahead scheduling scale and the intraday adjustment scale. In the day-ahead scheduling scale, the uncertainties of CCHP microgrid are introduced by describing the day-ahead forecast data as interval numbers with upper and lower bounds. The scheduling decision-making variables and the interaction power with power grid are obtained by considering the regulation cost in the worst-case scenario. In the intraday scheduling operation scale, the rolling optimization strategy is applied to obtain intraday optimal power allocation by minimizing the scheduling cost at each prediction horizon based on the intraday ultra-short-term forecast data, which preserves the day-ahead scheduling decision. Finally, the power deviation of the intraday ultra-short-term optimal power allocation is smoothed based on real-time operating data. The simulation results demonstrate that the proposed strategy can effectively reduce the operating cost and improve the robustness for the coordinated operation of CCHP microgrid.

Research on Microgrid Scheduling Based on Improved Crow Search Algorithm

Considering the characteristics of distributed power in microgrid, in order to maximize the advantages of distributed power generation technology in economy, environment, and energy, a multiobjective dispatching model of microgrid is proposed under the condition of satisfying system constraints and considering the operating costs and environmental costs of microgrid. The crow search algorithm (CSA) has the advantages of less parameter setting, simple implementation, and strong optimization ability and is often used in theoretical analysis and practical engineering applications. However, its disadvantage is that crows only search for candidate solutions according to their own experience, and their development ability is poor, especially for solving high-dimensional functions. In order to overcome these shortcomings, an improved crow search algorithm (CSA-PSO) is proposed based on the particle swarm optimization (PSO) algorithm. The two main improvements are as follows: (1) in order to avoid the blind selection of crow in search, the global optimal solution is adopted to modify the solution search equation to guide the search of new candidate solutions, so as to improve the development ability; (2) introducing a levy flight strategy to improve the single search mechanism of the CSA. In order to verify the performance of the CSA-PSO algorithm, 17 benchmark functions are simulated with other intelligent algorithms. The results show that the CSA-PSO algorithm has a good optimization effect in search accuracy, convergence speed, and robustness. Finally, this algorithm and other five algorithms are applied to the optimal scheduling problem of microgrid. By solving the objective function, the optimal scheduling output scheme of each distributed power supply is obtained. By using the CSA-PSO algorithm, the total operating cost of microgrid is reduced by at least 21.5%, which further verifies the effectiveness of the CSA-PSO algorithm.

Optimal scheduling of reconfigurable microgrids incorporating the PEVs and uncertainty effects

Here, an intelligent optimization method for optimal switching in reconfigurable microgrids is proposed to reduce the cost of microgrids with the presence of distributed generation resources and electrical vehicles. The suggested model deploys the point estimation to manage the uncertainties in the microgrid. The uncertainties are due to load demand, market price, electric vehicle charging and discharging, wind turbine production capacity and photovoltaic cells. The formulation structure is in the form of a nonlinear and single-objective problem that minimizes the total microgrid asking price by observing the constraints in the problem. In this research, an improved algorithm is used to solve the optimization problem to advance the performance of the process. The capability and efficiency of the proposed method have been investigated on the IEEE 33-Bus standard test network and the simulation results have shown that optimal switching can be useful in more fitting power distribution and reduction of microgrid cost with the presence of electric vehicle and various scattered products.

A cost-effective approach for optimal energy management of a hybrid CCHP microgrid with different hydrogen production considering load growth analysis

An optimum design and energy management of various distributed energy resources is investigated in a hybrid microgrid system with the examination of electrical, heating, and cooling demand. This paper suggested an optimal approach to design and operate a microgrid incorporating with battery energy storage, thermal energy storage, photovoltaic arrays, fuel cell, and boiler with minimization of the total operational cost of the hybrid microgrid. Two different hydrogen production methods are considered to assure the advantage of the developed proposed methodology. Furthermore, besides natural gas, residential and municipal wastes are collected and are utilized to produce electricity in fuel cell units. Load growth for different type of loads is also considered. The new number of households are added to the proposed system in different years and the proposed program is determined the optimum size of each employed resources to add each year for satisfying the total demand. To find out the optimum energy management and the optimum capacity of each employed distributed energy resources, a meta-heuristic Particle Swarm Optimization Algorithm is utilized. It is concluded from the results that by utilizing residential waste, the amount of natural gas consumption by fuel cells is reduced about 6.2%, and by utilizing residential plus municipal waste, the reduction is about 26.7%. It is also observed that the amount of CO2 emission is reduced significantly (46.8%) in the case of utilization of produced heat by fuel cells. Finally, the results confirmed the efficacy of the suggested optimal energy management of the hybrid microgrid.

Bi-Level Energy Management Model of Grid-Connected Microgrid Community

As the proportion of renewable energy power generation continues to increase, the number of grid-connected microgrids is gradually increasing, and geographically adjacent microgrids can be interconnected to form a Micro-Grid Community (MGC). In order to reduce the operation and maintenance costs of a single micro grid and reduce the adverse effects caused by unnecessary energy interaction between the micro grid and the main grid while improving the overall economic benefits of the micro grid community, this paper proposes a bi-level energy management model with the optimization goal of maximizing the social welfare of the micro grid community and minimizing the total electricity cost of a single micro grid. The lower-level model optimizes the output of each equipment unit in the system and the exchange power between the system and the external grid with the goal of minimizing the operating cost of each microgrid. The upper-level model optimizes the goal of maximizing the social welfare of the microgrid. Taking a microgrid community with four microgrids as an example, the simulation analysis shows that the proposed optimization model is beneficial to reduce the operating cost of a single microgrid, improve the overall revenue of the microgrid community, and reduce the power interaction pressure on the main grid.

Multi-objective configuration of an intelligent parking lot and combined hydrogen, heat and power (IPL-CHHP) based microgrid

Recently, financial issues have been considered as the main aspects of microgrid (MG) evaluation in the literature. In this study, the optimal configuration of the MG has been calculated by presenting a reliability-constrained optimization model. In this optimization approach, the MG units are considered in full available state and random outage state through the planning horizon. To model a proposed MG in details, its uncertainties are formulated in the main function. A combination of Latin hypercube sampling (LHS) algorithm and K-means clustering algorithm is applied to generate all uncertainties. The proposed model simultaneously optimizes two objectives, namely, economic costs and emission performance. Time of use (TOU) based demand response (DR) program has been employed for optimal management of the demand side. At first, the bi-objective function is converted to a sequence of single-objective constrained problems by employing ε-constraints. All Pareto front solutions are obtained by utilizing GAMS for solving the developed mixed-integer linear programming (MILP) model. To make a trade-off among solutions, the max-min fuzzy decision-making method has been used. Due to the positive effect of the DR program on the configuring problem, the total emission and economic costs of MG have been reduced up to 4.01% and 1.72%, respectively.

Robust Microgrid Scheduling Considering Unintentional Islanding Conditions

This work presents a novel microgrid scheduling model considering the stochastic unintentional islanding conditions as well as forecast errors of both renewable generation and loads. By optimizing the dispatch of distributed energy resources (DERs), utility grid, and demand, the proposed model is targeted to minimize total operating cost of the microgrid, including start-up and shut-down cost of distributed generators (DGs), operation and maintenance (O&M) cost of DGs, cost of buying/selling power from/to utility grid, degradation cost of energy storage systems (ESSs) and cost associated with load shedding. To capture the stochastic unintentional islanding conditions and conventional forecast errors of renewable generation and loads, a two-stage adaptive robust optimization is proposed to optimize the objective function in the worst case scenario of the modeled uncertainties. The proposed optimization is solved with the column and constraint generation (C&CG) algorithm. The result obtained ensures robust microgrid operation in consideration of all possible realization of renewable generation, demand and unintentional islanding condition. The proposed model is validated with results of case studies on a microgrid consisting of various DGs and ESSs.

Blockchain Power Trading and Energy Management Platform

In this study, artificial intelligence, the Internet of things, and blockchain technology were combined to develop a vehicle-to-everything blockchain power trading and energy management platform with the objective of enabling multi-level power transactions for electric vehicle charging stations in or between commercial buildings. The proposed platform considers green power transactions across microgrids, bidirectional power and green power charging and discharging transactions in microgrids, demand response bidding, and vehicle-to-vehicle emergency rescue charging. This innovative architecture operates smart contracts and distributed ledgers independently; uses smart contracts to handle bidding, matching, and settlement of the power trading platform; and simultaneously employs the cloud and local distributed ledger nodes to chain the detailed real-time transaction information and power data on the ledger. In this manner, the security and fairness of data can be ensured; furthermore, a large amount of chaining information can be processed, and the chaining latency can be shortened. In addition, the communication protocols are integrated through the artificial intelligence of things. An energy management system (EMS) is proposed, which can perform optimal charging and discharging scheduling according to the power transaction matching results and then control charging piles and energy storage system devices. It can reduce the operating cost of microgrids in commercial buildings, increase the efficiency of green power utilization, and reduce power loss. In this system, power transactions are automatically and simultaneously executed in the EMS to achieve real-time supply and demand balance of regional microgrids.

Peer-to-peer multi-energy distributed trading for interconnected microgrids: A general Nash bargaining approach

Energy sharing has significant potential in improving system energy utilization, improving economic efficiency, and reducing the impact on power system. The multi-microgrids distributed coordinated operation model is proposed in this paper to provide a reference for the coordinated operation of multi-microgrids considering factors such as electric and heat demand response, multi-energy interaction and uncertainties. First of all, the system operation framework is introduced and the function of each part is analyzed. Secondly, the multi-microgrids cooperative operation model is established, and generalized Nash bargaining method is used to assign total operating cost to ensure the enthusiasm of each microgrid to participate in cooperation. Then, considering the uncertainty of wind power and load in microgrids, a stochastic programming model based on conditional value-at-risk is established. Furthermore, alternate direction multiplier method is used to solve the proposed model to protect the data privacy of each microgrid participating in cooperation. Finally, through the analysis of calculation example, it is verified that the proposed model can reduce the operating cost of each microgrid and maintain enthusiasm of each microgrid for cooperation. In addition, the convergence and accuracy of algorithm are explained, and the influence of different connection modes and the interaction limit of electric and thermal power on the operating cost of microgrids is compared. Moreover, stochastic programming model also provides a reference plan for the trade-off between economic benefits and risk level of multi-microgrids system.

P2H Modeling and Operation in the Microgrid Under Coupled Electricity–Hydrogen Markets

The uncertainty and volatility of wind power have led to large-scale wind curtailment during grid connections. The adoption of power-to-hydrogen (P2H) system in a microgrid (MG) can mitigate the renewable curtailment by hydrogen conversion and storage. This paper conducts unified modeling for different types of P2H systems and considers the multi-energy trading in a hydrogen-coupled power market. The proposed bi-level equilibrium model is beneficial to minimize the energy cost of microgrids. Firstly, a microgrid operation model applied to different P2H systems including an alkaline electrolysis cell (AEC), a proton exchange membrane electrolysis cell (PEMEC), or a solid oxide electrolysis cell (SOEC) is proposed at the upper level. Secondly, an electricity market–clearing model and a hydrogen market model are constructed at the lower level. Then, the diagonalization algorithm is adopted to solve the multi-market equilibrium problem. Finally, case studies based on an IEEE 14-bus system are conducted to validate the proposed model, and the results show that the microgrid with a P2H system could gain more profits and help increase the renewable penetration.

Optimal cost and feasible design for grid-connected microgrid on campus area using the robust-intelligence method

Abstract In this paper, a robust optimization and sustainable investigation are undertaken to find a feasible design for a microgrid in a campus area at minimum cost. The campus microgrid needs to be optimized with further investigation, especially to reduce the cost while considering feasibility in ensuring the continuity of energy supply. A modified combination of genetic algorithm and particle swarm optimization (MGAPSO) is applied to minimize the cost while considering the feasibility of a grid-connected photovoltaic/battery/diesel system. Then, a sustainable energy-management system is also defined to analyse the characteristics of the microgrid. The optimization results show that the MGAPSO method produces a better solution with better convergence and lower costs than conventional methods. The MGAPSO optimization reduces the system cost by up to 11.99% compared with the conventional methods. In the rest of the paper, the components that have been optimized are adjusted in a realistic scheme to discuss the energy profile and allocation characteristics. Further investigation has shown that MGAPSO can optimize the campus microgrid to be self-sustained by enhancing renewable-energy utilization.

Energy management system for optimal cost and storage utilization of renewable hybrid energy microgrid

This paper proposes an improved energy management system to reduce the energy cost and the storage utilization of a hybrid solar and wind energy microgrid with battery storage and grid-connected option. The performance improvement relies on a parameter, called the contribution factor, that provides an adequate amount of the required power from the battery based on the electricity price of the main grid. Different profiles, for that factor, are investigated to enhance the system performance. Load shifting mechanism has been integrated into the energy management to enhance the energy cost. The system uses the efficiencies of the components to accurately compute the energy cost. Simulations have been performed for different rates of the load demand and results have shown the superiority of the proposed energy management system compared to the conventional one with respect to reducing the energy cost, the battery degradation cost and increasing the battery lifespan. From the results, it has been found that using a linear profile for the contribution factor and the load shifting will result in daily costs of $68.27 and $0.81 for the energy and the degradation, respectively. These costs represent a 25.66 % decrease in energy cost and a 91.72 % decrease in battery degradation cost compared to the conventional energy management system where the grid electricity and the load shifting are not considered in the usage of the battery storage.