Microgrid Planning Research Articles

Optimal planning and operation of multi-carrier networked microgrids considering multi-energy hubs in distribution networks

In this paper, we propose a new scheme for designing multi-carrier networked microgrids. Various multi-energy carriers are utilized to form energy hubs which exchange energy with each other and the main electricity and gas networks. The proposed model considers renewable generation, diesel generators, fuel cells, energy storages, combined heat and powers, boilers, combined cooling heat and powers, and power to gas converters. The seasonal load patterns are considered to design the optimal capacity of combined cooling, heating, and powers. The main objective of the proposed model is to operate the integrated hubs with minimum cost, meet the consumer’s required energies, reduce the emissions, and create a bi-directional interaction between electricity, and gas networks using power to gas converters. While all electrical, mechanical, and technical constraints are satisfied. To achieve these improvements, industrial and residential hubs interact with other multi-carrier microgrids to determine the optimal operation of hubs. The proposed model is tested on a standard case study and results show when hubs integrate about the total daily cost of each microgrid has been improved about 200 $. Besides, the thermal energy not supply in microgrid 4 has been improved from 4.61 MWh to 0.45 MWh.

Optimal simultaneous allocation of passive filters and distributed generations as well as feeder reconfiguration to improve power quality and reliability in microgrids

Despite numerous studies on optimal planning of Microgrids (MGs), little attention has been paid to provide an enviro-techno-economic model for planning the balanced and unbalanced MGs. This paper proposes an integrated model for simultaneous optimal allocation of passive filters and the inverter-based renewable Distributed Generations (DGs), as well as Distribution Feeder Reconfiguration (DFR) in balanced and unbalanced MGs. The proposed model is formulated as a Multi Objective Problem (MOP), which considers the revenue of the MG operator (MGO), reliability index, unbalanced voltage of buses, and greenhouse gas emissions. The MOP is solved by Multi-Objective Covariance Matrix Adaption-Evolution Strategy (MOCMA-ES) algorithm and a set of Pareto solutions are achieved subject to diverse technical constraints such as Total Harmonic Distortion (THD). Then, the best solution is selected based on the Mean Ideal Distance (MID) index. The performance of the proposed model was assessed on balanced 33-bus and unbalanced 25-bus MGs and results of the MID index were compared to the best solutions when the preference of the MGO was to achieve maximum revenue and minimum emissions. Simulation results illustrated the efficiency of the proposed integrated model with the improvement of the MGO benefits, the power quality, and the reliability index.

Optimal sizing of distributed energy resources and battery energy storage system in planning of islanded micro-grids based on life cycle cost

In islanded micro-grids, the electrical loads are only supplied from distributed energy resources (DER) because these systems cannot connect to the main power grid directly. Hence, this research proposes a planning framework to optimize the power and technology of the DER that the objective function is minimizing the life cycle cost of islanded micro-grids during the planning period. The DER that utilizes renewable energy has many advantages and is often considered to invest in micro-grids. However, the output power of them often changes and depends on the primary energy, such as wind and solar irradiance. Hence, the power and capacity of the battery energy storage system (BESS) are simultaneously considered in planning problems to improve the reliability and effectiveness of the islanded micro-grids. Additionally, the natural intermittent generation power of the renewable energy sources and uncertainty of the demand loads are modeled by probability density functions then they are integrated into the planning model. The binary variables are also integrated into a mixed-integer nonlinear programming model to select the modular type corresponding to the rated power of the DER with the objective function, which is the life cycle cost of the project. Similarly, the rated power and capacity of the BESS are also discrete values thus the binary variables are utilized to select the sizes and charge or discharge modes of the BESS. The constraints on the energy balance, limit power and capacity of equipment are taken into account in this model. The general algebraic modeling system (GAMS) is applied to solve the optimization problem and the simulation results of the test structure show the effectiveness of renewable energy resources and the BESS in planning islanded micro-grids.

Computation of Power Generation of Green Power Unit based on Probability Distribution Parameter

Today’s power systems era is speedily going towards natural sources of power. The power system is also in the mode of decentralization. Decentralize means to split a large power system into small, safe and smart power grid or to create microgrid. This decentralization required separate control of each part of the power system. It can reduce the complexity of the large power system; provide efficient and economical installation and easy operation. To keep our globe healthy; green sources of electrical energy like solar, wind tidal, biomass are the best option for the power sector. Integrating green power sources in a decentralized power system needs better planning and upgraded technology. To deal with uncertainties of Green power sources, the first step is to estimate output power. In this paper output power with the uncertain nature of sources is estimated and analyzed to integrating green power sources. For it, the statistic model of solar and wind power generation has been discussed. Probable distribution of wind speed and solar irradiation is found using available mean data. Output power Generation of wind and solar sources are estimated based on the probability distribution curve and power equation. It has analyzed for different value of shape parameter Here mean wind speed and mean solar irradiation data of one town of Gujarat state, India has been collected for the planning of microgrid.

Design of an Incentive-based Demand Side Management Strategy for Stand-Alone Microgrids Planning

This paper presents the design of a Demand Side Management Strategy (DSMS) using Integer Linear Programming (ILP) for the planning of Stand-Alone Microgrids (SAMG). The DSMS uses a reduction in the tariff to increase the consumption and an extra payment added to the fare to penalize it. To evaluate the performance of the DSMS in the planning of the SAMG, the proposed methodology must be able to solve the sizing and the optimal dispatch for the DSMS simultaneously. In this regard, we introduce a nested simulation model that works inside of the sizing formulation of the SAMG. In one hand, the sizing algorithm use a heuristic formulation that mimics the behavior of the gradient descent method for discrete functions. In the other hand, the ILP approach of the DSMS is formulated as a function of the installed capacities and unitary installation costs to facilitate the cooperation between both algorithms. Afterward, the simulation model is used to obtain the installation and operational costs. This process repeats until the sizing algorithm finds the less expensive combination of capacities that can feed the electrical demand. Simulation results show reductions in the values of the sizing and the Levelized Cost of Energy when the planning of the SAMG uses the designed DSMS.

Low-Frequency Stability Analysis of Inverter-Based Islanded Multiple-Bus AC Microgrids Based on Terminal Characteristics

For system planning of three-phase inverter-based islanded ac microgrids, the low frequency instability issue caused by interactions of inverter droop controllers is a major concern. When internal control information of procured commercial inverters is unknown, impedance-based small-signal stability criteria facilitate prediction of resonances in medium and high frequency ranges, but they usually assume the grid fundamental frequency as constant and thus they are incapable of analyzing the low-frequency oscillation of the fundamental frequency in islanded microgrids. Aiming at solving this issue, this paper proposes two stability analysis methods based on terminal characteristics of inverters and passive connection network including the dynamics of the fundamental frequency for analysis of low-frequency stability in islanded multiple-bus microgrids. Based on the Component Connection Method (CCM) to systematically separate inverters from the passive connection network, a general approach is developed to model the microgrid as a multiple-input-multiple-output (MIMO) negative feedback system in the common system d-q reference frame. By applying the generalized Nyquist stability criterion (GNC) to the return-ratio and return-difference matrices of the MIMO system model, the low-frequency stability related to the fundamental frequency can be analyzed using the measured terminal characteristics of inverters. Analysis and simulation of a 37-bus microgrid verify the effectiveness of the proposed stability analysis methods.

Optimal planning of multiple energy flows for microgrid with renewable energy sources

With the gradual depletion of fossil energy and the increasingly serious environmental pollution problems, the combined cooling heating and power (CCHP) microgrid with renewable energy sources which with high energy efficiency, less pollution emissions and flexible operation control have become research hotspots. In this paper, the optimal planning of CCHP microgrid with renewable energy sources is studied, and the mathematical model of optimal configuration of the system is established with the minimum total cost as the objective function, then the separated production (SP) system is used as a reference to compare the optimal configuration results of the CCHP microgrid system under the two modes of following the thermal load (FTL) and following the electric load (FEL). After that, the optimization configuration results of the two schemes of whether the CCHP microgrid system is equipped with the thermal storage tank (TST) in the same operating mode are compared. The simulation result shows that the system with TST under the FEL mode is more economical, energy-saving and environmentally friendly. Finally, the effects of natural gas price and electricity price on the optimal configuration results of the system in two operating modes are studied.

Optimal planning of clustered microgrid using a technique of cooperative game theory

In this paper a cooperative type of game theoretical technique is proposed at the planning stage to model a grid-connected clustered microgrid. In order to make it more applicable, the selected microgrid consists of different combinations of generation resources like wind turbines, solar cells, and batteries. In the game model, generation resources are considered as players and their profit as the payoff. The technique of the Nash bargaining solution is adopted in this study for capacity allocation of generation resources and batteries, and also to maximize the annual profit of individual microgrids and its cluster. As a cooperative game model, all possible coalitions are discussed between the players to find their optimum sizes, and most the suitable one is selected based upon the game theory technique and maximum payoff value. For this purpose, a particle swarm optimization (PSO) algorithm is developed to find the most feasible Nash bargaining solution using MATLAB software. In the simulations and for the system analysis, the realistic electrical load data and weather forecast is taken for a remote town Mount Magnet in Western Australia. Finally, a sensitivity analysis is performed to validate and show the reasonability of the optimized results concerning the proposed clustered system.

Harnessing demand-side management benefit towards achieving a 100% renewable energy microgrid

Optimal sizing with energy management strategy as a transition pathway towards a sustainable 100% renewable energy-based microgrid is investigated in this paper. Due to the challenges of intermittency of renewable energy, microgrid operations are complicated. Hence, in order to overcome some of the challenges facing microgrid planning and operations, optimal capacity sizing incorporated with energy management strategy considering time-ahead generation prediction is proposed. The system model consists of wind turbine (WT), solar photovoltaic (PV) and battery energy storage system (BESS). The generation forecasting output is used to reschedule the flexible demand resources (FDR) to reduce the mismatch between power demand and supply, and optimal sizing of components is performed jointly to determine the optimal capacity values of the PV, WT, and BESS for minimal investment costs. The optimization results for the scenarios with and without load shifting effects of FDRs are determined and analyzed for the case study. From the results obtained, the application of demand scheduling program using the generation forecasting outputs resulted in a cost-saving of 12.41%. The forecasting model is implemented using a random forest algorithm on python platform and the mixed-integer linear program on MATLAB® environment is used to model and solve the capacity sizing problem.

An Optimal Multivariable Constrained Nonlinear (MVCNL) Stochastic Microgrid Planning and Operation Problem With Renewable Penetration

Microgrids (MGs) are promising archetypes for the penetration of renewable energy sources (RES) into the system for reducing carbon footprints. This article presents a cost-based optimization model using a stochastic programming to obtain the optimal size of distributed energy resources including RES. Uncertainties in the solar output and the load demand data are considered using multiple scenarios with random variations, which are based on beta distribution. The objective function is formulated as a cost-based multivariable constrained nonlinear convex programming problem. Other than planning costs, the objective function also includes per day running or operational costs such as unit commitment costs as well as the switching costs associated with the dispatchable units. The overall problem is simulated under MATLAB environment. Quantitative results indicate the impact of operational paradigm along with the planning of an MG in the form of cost analysis with different reliability conditions. An assessment of results in tabular form is presented, which incorporates the optimal sizing of MG under the different system reliability conditions and constraints. Some additional cases pertain to the generation and the load demand scenarios have also been included in the article.

Planning Framework for Optimal Resource Utilization Strategy in Microgrid

The incorporation of distributed energy resources and technical innovations has restructured the electrical power system network from conventional grid to microgrid. Consequently, significant impacts on both distribution and utilization of power have been observed triggered by diverse operating condition and enhanced power demand. In planning of microgrid, energy management is a critical issue which is related to efficient utilization of available resources at minimum operating cost. Distributed and intermittent nature of generations in microgrid demands efficient and optimal allocation of existing resources. An allocation technique is necessary to attain optimal utilization of assets and to accomplish decision making for optimal power flow route through shortest path. This paper proposes a multi-objective optimization-based resource allocation technique to efficiently control available generation at any load condition to achieve minimum operating cost while maintaining all system constraints. In proposed framework, line flows are maximized to its load-carrying capability and by controlling power flow through shortest possible path. Proposed methodology suggests proper controller position to sustain desired power flow through the shortest path between each generator and load pair. An interconnected microgrid network topology is considered to demonstrate the proposed methodology.

Sizing of Hybrid Islanded Microgrids using a Heuristic approximation of the Gradient Descent Method for discrete functions

Hybrid microgrids can handle fuel scarcity, reduce harmful emissions, increase flexibility, efficiency, and reliability. Nevertheless, these benefits are strongly related to the sizing and the energy management strategy (EMS) of the microgrid. In this regard, the sizing and the EMS acquire high importance for the planning of stand-alone microgrids. The present paper aims to design a sizing methodology that has a nested simulation model for power and energy balances. The sizing uses a heuristic approximation of the gradient descent method for discrete functions. The simulation model can evaluate the operation of the microgrid, even with EMSs using optimal criteria. A study case evaluation shows the effectiveness of the proposed algorithm compared to a traditional heuristic technique as Particle Swarm Optimization and an exhaustive search, which prove the feasibility of the algorithm to be used in stand-alone microgrid planning.

Linear three-phase power flow model for autonomous microgrid systems

In this work, the authors propose a linear three-phase power flow model for droop-controlled autonomous microgrids. The proposed linear power flow model adopts a set of linear algebraic equations and can, with small errors, be compared to the detailed nonlinear model, account for the lack of a slack bus, the variability of autonomous microgrid frequency and the operation of distributed generation units in droop control mode. The proposed linear model can as such provide a fast and accurate power flow solution and has great potential in the stochastic planning and online operational applications of autonomous microgrids, as well as in linear or convex optimal power flow and probabilistic load flow analysis. The accuracy and effectiveness of the proposed linear power flow model are validated through different case studies considering balanced and unbalanced test systems.

Day-ahead generation planning and power reserve allocation with a flexible storage strategy

High levels of renewable generation incorporation increase the flexibility requirements of the electrical system in response to fast and large variations in load and renewable energy output. Advanced power system operational methods and algorithms are required to schedule the generating units with more reliability and efficiency. To deal with uncertainties in the generation planning of an urban microgrid, this study presents a storage control strategy for reserve provision in a multi-objective scenario-based stochastic optimisation algorithm. Power reserve allocation is optimised while uncertainties from renewable energy and load demand are taken into account. Results confirm that the presented algorithm ensures that storage-hybridised RESs contribute to the primary source of reserve capacity, and the performance in terms of security level is highlighted.

Study on the Microgrid Pluripotent Complementary and Comprehensive Economic Optimization Planning Based on Virtual Energy Theory

In this paper, a comprehensive economic allocation method based on virtual energy theory for microgrid complementary power supply is proposed, which is successfully applied to the multi energy complementary planning and comprehensive economic planning of an island microgrid. In order to evaluate and optimize the microgrid in the planning stage more objectively based on the characteristics of various combinations of renewable micro generation and flexible operation modes, a new method of the micro-grid comprehensive economic configuration and optimization of the distributed generation is proposed based on virtual energy theory. Firstly, the Micro-grid model structure based on the virtual energy theory is given. Secondly, the virtual energy conversion model of different distributed power generations is also studied. The micro-grid pluripotent complementary and comprehensive economic optimization model (PCCEP) based on virtual energy theory are reached too. Combined with a practical example, a PCCEP method of the micro-grid based on the Homer software platform is discussed. The case study indicates that the method can avoid the uncertainty of the decision-making factors and the subjectivity of expert evaluation. It is shown that the new method is a practical solution for microgrid comprehensive economic optimization planning.

A Multi-Objective Optimization Scheme for Resilient, Cost-Effective Planning of Microgrids

Natural disasters and cascading events have historically caused severe disruptions of the electric power system. For instance, in 2012, hurricane Sandy left over 8 million people in darkness and caused total damage costing $65 billion. Under emergency conditions, microgrids can help the electric power system recover critical loads, such as hospitals, data centers, and water pumping stations. While the literature has mostly focused on the utilization of existing microgrids, the idea of planning for future microgrids in combination with switching operations to make the grid more resilient against devastating events is investigated in this study. This work aims to simultaneously maximize the resiliency of distribution networks - in terms of service to the critical loads - and minimize the dispatchable generation capacity of microgrids. The considered microgrid model entails dispatchable power generators, renewable energy resources, and electrical energy storage systems (ESS) to serve consumers. Considering the topological and operational limitations, the robust optimization scheme optimizes the objectives by the effective selection of the node for microgrid connection and the minimum change in its generation capacity. While the problem is modeled as a multi-objective, mixed-integer linear programming (MO-MILP) problem, the results show more than 99% accuracy compared to the exact results of an exhaustive search algorithm. Numerical tests are performed on the IEEE 37-node test feeder and the IEEE 123-node test feeder to assess the proposed method's performance. Given the accuracy, computation time efficacy, and the generic formulation of the problem, the optimization scheme can be easily applied to any real network.

Design of an Incentive-based Demand Side Management Strategy using ILP for Stand-Alone Microgrids Planning

Design of an Incentive-based Demand Side Management Strategy using ILP for Stand-Alone Microgrids Planning

A systematic decision-making approach for planning and assessment of hybrid renewable energy-based microgrid with techno-economic optimization: A case study on an urban community in Egypt

This paper proposes a systematic and integrative decision-making approach for efficient planning and assessment of hybrid renewable energy-based microgrid (HRE-MG) systems. The approach is combined with an extensive techno-economic optimization analysis. The proposed methodology is validated with an actual case study on an urban city in Egypt, with the developed HRE-MG comprising solar, wind, diesel, and fuel cell energies. Different hybridization scenarios are modeled, evaluated and compared to obtain the most practicable solution with minimum cost and emission impact. The obtained results reveal that solar, wind, fuel cell show optimal configuration plan that yields a minimum net present and energy costs. Also, it achieves a significant reduction in carbon gases and increases system renewable fraction compared to other scenarios. Further, the reliability of the proposed hybrid energy system is examined under loss of power supply probability concept. A sensitivity analysis is then conducted to verify the model accuracy and examine the cost performance of the designed system concerning uncertain parameters.

Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgrid Installation: A Comparative Study

Microgrid is becoming an essential part of the power grid regarding reliability, economy, and environment. Renewable energies are main sources of energy in microgrids. Long-term solar generation forecasting is an important issue in microgrid planning and design from an engineering point of view. Solar generation forecasting mainly depends on solar radiation forecasting. Long-term solar radiation forecasting can also be used for estimating the degradation-rate-influenced energy potentials of photovoltaic (PV) panel. In this paper, a comparative study of different deep learning approaches is carried out for forecasting one year ahead hourly and daily solar radiation. In the proposed method, state of the art deep learning and machine learning architectures like gated recurrent units (GRUs), long short term memory (LSTM), recurrent neural network (RNN), feed forward neural network (FFNN), and support vector regression (SVR) models are compared. The proposed method uses historical solar radiation data and clear sky global horizontal irradiance (GHI). Even though all the models performed well, GRU performed relatively better compared to the other models. The proposed models are also compared with traditional state of the art methods for long-term solar radiation forecasting, i.e., random forest regression (RFR). The proposed models outperformed the traditional method, hence proving their efficiency.

Multi-period planning of multi-energy microgrid with multi-type uncertainties using chance constrained information gap decision method

In this paper, we study the multi-period planning problem of multi-energy microgrids considering the long-term uncertainty (i.e., the declining trend of battery storage investment cost) and the short-term uncertainty (i.e., renewable energy generation and electrical/heat load). We first present the joint deterministic multi-period planning approach for multi-energy microgrid coupling electricity and heat carriers. Then, an information gap decision (IGD)-based multi-energy microgrid multi-period planning model dealing with the long-term uncertainty is proposed, and the proposed model is further converted into a mixed integer linear planning (MILP) IGD-based planning model. Next, to coordinate the long-term uncertainty and the short-term uncertainty in multi-energy microgrid planning problems, we develop a chance constrained (CC) IGD-based multi-period planning model and then convert such model into a MILP CC-IGD equivalence. Finally, the strengthened bilinear Benders decomposition (SBBD) algorithm is adopted to efficiently solve our proposed MILP CC-IGD model for large-scale multi-energy microgrid planning problems. Our numerical results demonstrate the advantage of the joint planning of electricity and heat supply systems in multi-energy microgrids. Case studies verify the effectiveness of considering multi-type uncertainties in multi-energy microgrid planning, especially the declining trend uncertainty of battery storage investment cost. Experimental results also show that the SBBD algorithm is more efficient on computing our proposed MILP CC-IGD model compared to commercial solvers, such as CPLEX.