Multi-carrier Microgrid Research Articles

Smart Frequency Control of a Multicarrier Microgrid in the Presence of V2G Electric Vehicles

In recent years, renewable resources have widely been used to provide necessary energy due to the increasing fossil fuel prices, environmental pollution concerns, and the necessity to meet the growth in energy demands. The output of renewable resources especially solar and wind energies is associated with meteorological parameters, so their reliability creates many challenges for the energy sector. The consumption peak of the gas network is taken into account to adjust the frequency of the microgrid (MG). Both gas network load and electric load distributions are adjusted at the same time. In a multicarrier network, the frequency is regulated in a nonlinear manner. Meanwhile, new necessary loads for production and electric vehicles have imposed new loads on the power network; if proper management is not performed to respond to these new loads, the increase of network frequency deviations may lead the network to fail and even break down. In this paper, a network of various sources including the wind turbine, solar panel, storage (battery and flywheel), electric vehicle (EV), diesel generator (DG) electric power generation, and multicarrier energy hub (MCEH) with combined heat and power (CHP) was designed to examine vehicle-to-grid (V2G) electric vehicles. The ANFIS adaptive fuzzy control method was used to provide a fine-tuning frequency of the network. A comparison between the suggested approach and a fuzzy controller system was carried out to examine the superiority of the introduced approach to the frequency control. The simulations were obtained using MATLAB/SIMULINK software. The simulation outcomes indicated that the SMART controller can achieve good efficiency in frequency regulation and reliable output power in the examined microgrid. Further comparison in terms of effective (RMS) values and maximum frequency deviation indicates the superior performance of the proposed method over the fuzzy method.

Optimal day-ahead energy planning of multi-energy microgrids considering energy storage and demand response

Due to the environmental and economic advantages of combined heat and power (CHP) units, their use in power grids has expanded. The entry of CHP into power systems increases the complexity of the economic power flow problem. This complexity is due to the introduction of multiple constraints into problem. A mere electricity supply is not optimal in today's networks, and energies such as heat, power and gas must be planned and managed simultaneously as an energy hub. Therefore, in this paper, an intelligent multi-energy microgrid (MG) consisting of power generation units, CHP units and gas units is modeled for day-ahead energy management (DAEM). The economic distribution problem focuses on the amount of power generation, heat and gas of the units in the system. In contrast, the total generation cost of the system is minimized, and all the equality and inequality constraints of the problem are observed. The proposed microgrid includes various energy-dependent equipment such as CHP units, gas boilers, electricity-to-gas units, power and heat storage units and electric heat pumps. Also, price-based load management was included to reduce costs due to the transfer of information between the consumer and the generator in the context of smartization. Since the above problem is difficult to solve due to various constraints and decision parameters, a newly developed optimization method based on water flows was proposed. The simple movement of water flows on the ground is efficient and optimal and always follows the shortest and fastest path to reach the deepest point. In the proposed algorithm, simple movements of water in routing, a change of direction and even the creation of rapids and vortices were simulated as various mathematical operators. Finally, the proposed model and method were examined in different scenarios. The numerical outcomes demonstrated that, the proposed modeling framework is superior to hub-based multi-carrier microgrid models in terms of power system security. The sensitivity of operational expenses to changes in initial values of energy storage systems (ESS) and thermal storage system (TSS) is proved that the cost of operation reduces as the baseline values of ESS and TSS are reduced to 0.2% of the maximum capacity. Because DAEM performance is less flexible when the primary values are reduced by 0.2% of the maximum value, the system running expenses increase marginally.

RETRACTED: Renewable source uncertainties effects in multi-carrier microgrids based on an intelligent algorithm

High volatility and unpredictable behavior of renewable energy sources provide various challenges in the operation of power systems with maintaining the system's reliability. To solve the challenges of grid-connected renewable energy sources, microgrids (MGs) can play an essential role due to their tuning capability and flexibility. The fast growth of power-to-gas technology causes to support renewable energy integration and transforms the extra renewable generation to combined normal gas at a suitable time. The optimal type, size, and commissioning year of the accessible distributed energy resources through the economic application of nominated resources are evaluated to be applied in the suggested multi-carrier microgrid. This study focuses on the simultaneous operation of multi-carrier MGs and their equipment as an optimization problem. Small-scale energy sources and load demand in MGs are modeled to reduce costs of exchange between the main grid and MGs, as well as the load balance between MGs and load demand. Given that it is difficult to solve the desired problem considering various constraints, the developed meta-heuristic whale optimization algorithm (WOA) is used along with adaptive weighting coefficients and a local search operator. Moreover, this model investigates the unstable behavior of loads, renewables, and electricity prices using the probabilistic load flow method (PLF), considering multiple carriers simultaneously to match the real world. In this paper, MGs with multiple-carriers exchange energy with the upstream grid. The energy exchange between MGs is also considered. The results of simulations performed on the grid consisting of three interconnected multi-carrier MGs confirmed the effectiveness of the suggested model. Obtained numerical results prove the validity of the suggested multi-carrier microgrid expansion planning issue by economic, environmental, and technical points.

Optimal Investment Planning of Bankable Multi-Carrier Microgrid Networks

Multi-carrier microgrids are increasingly recognized to play a critical role in capturing the synergies between various vectors of energy, including electricity, gas, and heating/cooling systems, thereby providing an effective platform for handling the complexities associated with the dynamic interactions of the system components. While a recent, growing body of the literature has conclusively shown the effectiveness of multi-carrier microgrids in improving the business cases of localized energy solutions with high penetrations of renewables, the potential benefits associated with interconnecting neighboring multi-carrier microgrids to form networked systems are less well-explored. This is particularly important as networked multi-carrier microgrids capable of trading energy with each other during grid outages can significantly improve the resilience of the aggregated system, whilst at the same time reducing the total discounted costs. In response, this paper introduces an integrated planning framework, formulated as a mixed-integer linear programming problem, for the optimal planning of neighboring multi-carrier microgrids in the presence of demand-side flexibility resources with the aim of minimizing the total discounted cost of the networked microgrids. Based on the numerical evidence obtained from the application of the proposed method to a test-case system, the integrated planning optimization framework's effectiveness in improving the economics and resilience of a cluster of electrically coupled multi-carrier microgrids is validated. More specifically, the numerical simulations confirm the proposed model's effectiveness in a significant reduction of the under-utilized storage and dispatchable generation capacities, as well as the otherwise-curtailed non-dispatchable generations. Accordingly, the findings from this study have important implications for accelerating the transition to locally more reliable, affordable, autonomous microgrids.

Robust Optimal Control for Demand Side Management of Multi-Carrier Microgrids

This paper focuses on the control of microgrids where both gas and electricity are provided to the final customer, i.e., multi-carrier microgrids. Hence, these microgrids include thermal and electrical loads, renewable energy sources, energy storage systems, heat pumps, and combined heat and power units. The parameters characterizing the multi-carrier microgrid are subject to several disturbances, such as fluctuations in the provision of renewable energy, variability in the electrical and thermal demand, and uncertainties in the electricity and gas pricing. With the aim of accounting for the data uncertainties in the microgrid, we propose a Robust Model Predictive Control (RMPC) approach whose goal is to minimize the total economical cost, while satisfying comfort and energy requests of the final users. In the related literature various RMPC approaches have been proposed, focusing either on electrical or on thermal microgrids. Only a few contributions have addressed the robust control of multi-carrier microgrids. Consequently, we propose an innovative RMPC algorithm that employs on an uncertainty set-based method and that can provide better performance compared with deterministic model predictive controllers applied to multi-carrier microgrids. With the aim of mitigating the conservativeness of the approach, we define suitable robustness factors and we investigate the effects of such factors on the robustness of the solution against variations of the uncertain parameters. We show the effectiveness of the proposed RMPC approach by applying it to a realistic residential multi-carrier microgrid and comparing the obtained results with the ones of a baseline robust method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by the emerging need for effective energy management approaches in multi-carrier microgrids. The inherent difficulty of scheduling simultaneously the operations of various energy infrastructures (e.g., electricity, natural gas) is exacerbated by the inevitable presence of uncertainties that affect the inter-dependent dynamics of different energy resources and equipment. The proposed robust MPC-based control strategy allows the energy manager to effectively determine an optimal energy scheduling of multi-faceted system components, making a tradeoff between performance and protection against data uncertainty. The presented strategy is comprehensive and generic, as it can be applied to different microgrid frameworks integrating various types of system components and sources of uncertainty, while at the same time being implementable in any energy management system.

A scenario-based stochastic model for day-ahead energy management of a multi-carrier microgrid considering uncertainty of electric vehicles

This paper presents a scenario-based stochastic management algorithm to deal with scheduling the optimal operation of a multi-carrier microgrid (MG). The proposed framework of the multi-carrier MG comprises of wind turbine (WT), photovoltaic (PV) panel, fuel cell (FC), microturbine (MT), boiler, combined heat and power (CHP) unit, electrical load, thermal load, hydrogen load, electrical energy storage, H2 storage, and thermal storage. To cope with the uncertainties arisen by the renewable resources, electricity price, electrical load, and electric vehicle (EV), we proposed a scenario-based approach to model the uncertain parameters (wind speed, solar irradiance, electricity price, electrical load, EV arrival time, EV departure time, and EV traveled distance). The proposed algorithm initially estimates the uncertain parameters through fitting a probability distribution function (PDF) with the time series of historical uncertain parameters. Then, the scenario generation and reduction approach is applied to find the finite scenarios according to the fitted PDFs. The stochastic management algorithm is formulated as a cost minimization problem, where the demand response (DR) program is embedded in the formulation. The proposed framework is tested on a sample multi-carrier MG for two cases, namely, the application of stochastic energy management for a Multi-carrier MG with and without DR. Also, we perform a comparative study to show the benefits of the stochastic management algorithm along with a deterministic approach.

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.

A Probabilistic Model for Minimization of Solar Energy Operation Costs as Well as CO2 Emissions in a Multi-Carrier Microgrid (MCMG)

This paper proposes a probabilistic model with the aim to reduce the solar energy operation cost and CO2 emissions of a multi-carrier microgrid. The MCMG in this study includes various elements such as combined heat and power (CHP), electrical heat pump (EHP), absorption chiller, solar panels, and thermal and electrical storages. A MILP model is proposed to manage the commitment of energy producers, energy storage equipment, the amount of selling/buying of energy with the upstream network, and the energy consumption of the responsible electrical loads for the day-ahead optimal operation of this microgrid. The proposed operation model is formulated as a multi-objective optimization model based on two environmental and economic objectives, using a weighted sum technique and a fuzzy satisfying approach. In this paper, the 2 m + 1-point estimate strategy has been used to model the uncertainties caused by the output power of solar panels and the upstream power supply price. In order to evaluate the performance of the proposed model, and also for minimizing cost and CO2 emissions, the simulation was conducted on two typical cold and hot days. Numerical results show the proposed model’s performance and the effect of electrifying the heating and cooling of the microgrid through the EHP unit on greenhouse gas emissions in the scenarios considered.

Resilience-Oriented Planning of Multi-Carrier Microgrids under Cyber-Attacks

Microgrids are inherently subject to a variety of cyber-physical threats due to potential vulnerabilities in their cyber systems. In this context, this paper introduces a cyber-attack-resilient design of a multi-carrier microgrid to avoid the loss of critical loads. The objective of the proposed model is to minimize the total planning cost of multi-carrier microgrids, which incorporates the investment and replacement costs of distributed energy resources, operation and maintenance costs, peak demand charges, emission costs, unserved energy costs, and potential reinforcement costs to handle cyber-physical attacks. Not only is the proposed multi-carrier microgrid planning approach able to determine the optimal size of multi-carrier microgrids, but it also identifies and reinforces the system to handle cyber-physical attacks by serving critical loads. The proposed multi-carrier microgrid planning model is formulated as a mixed-integer programming problem and solved using the GAMS 24.1 software. To evaluate the effectiveness of the proposed integrated resource planning model, it is applied to a real-world industrial park test-case system. Numerical simulations demonstrate the effectiveness of the resilience-oriented multi-carrier microgrid planning model. Importantly, the simulation results indicate the economic viability of multi-carrier microgrids optimized by the proposed model. Also, the model sensitivity of various decision variables has been analyzed.

Robust optimal scheduling of CHP-based microgrids in presence of wind and photovoltaic generation units: An IGDT approach

Application of combined heat and power (CHP) units beside the distributed energy resources (DERs) persuades the power systems via the formation of multi-carrier microgrids (MGs). This paper presents the day-ahead scheduling of multi-carrier MGs. Renewable distributed generators are known as undeniable parts of modern power systems, which may increase the chanciness of the system. Information gap decision theory (IGDT) is applied to address the uncertainties of renewable sources. Moreover, a scenario-based stochastic approach is used to model the uncertainty of electricity prices in this method. Indeed a hybrid stochastic-IGDT-based optimization problem is proposed for optimal energy management of multi-carrier MGs. According to this method, the operation of multi-carrier MGs would be robust against uncertainties and contain a minimum profit. The proposed problem has been formulated as mixed-integer linear programming (MILP). Finally, the proposed energy management has been implemented into a sample multi-carrier microgrid. The results showed that how the MG operator’s risk-averse character can change her/his decision-making. • Robust Optimal Scheduling of CHP-based Microgrids. • Proposing an IGDT-based approach to handle uncertainties of renewable generations. • Proposing a single level model for the IGDT method to obtain a global optimum. • Considering demand response programs for both thermal and electrical loads.

Strategic bidding of a multi‐carrier microgrid in energy market

Microgrid can contribute to the day-ahead market by submitting bids to minimize its costs. The bidding problem is challenging due to various uncertainties. Thus, the present study provides a comprehensive optimal bidding strategy to determine the optimal power bids of a multi-carrier microgrid despite the interdependency of power and gas market prices on the day-ahead and real-time markets. In this work, the stochastic energy bidding in the proposed multi-carrier microgrid is solved via a two-stage procedure to benefit from day-ahead and real-time markets. In the first stage, the operator provides hourly energy bids to the distribution system operator regardless of uncertainty. Then, in the second stage, taking into account the confirmed day-ahead bids, the multi-carrier microgrid operator serves to balance the load in the real-time market stemmed from various uncertain parameters. This problem is solved as mixed-integer linear programming by CPLEX of the GAMS solver. A scenario reduction method is also employed to burden the complexity of the problem. Numerical results show the usefulness of the proposed model.

Enabling demand response for optimal deployment of multi‐carrier microgrids incorporating incentives

This paper inspects customer multi-carrier microgrid deployments' techno-economic viability and assists investors in deciding whether or not to invest in multi-carrier microgrid installations equipped with smart demand-side technologies. The solution of the proposed model determines the optimal mix and size of distributed energy resources, and identifies the ideal participation rate of potential responsive customers within the multi-carrier microgrid. The objective of the proposed model is to minimize the overall deployment cost comprising the investment and replacement of distributed energy resources, demand-side smart measurement and informing appliances, loan payoff, operation, maintenance, peak demand charge, energy demand shifting reward or penalty, emission, and unserved energy while ensuring the desired levels of reliability and online reserve. The model also considers incentive policies to encourage customers to install demand-side smart technologies to participate in demand response programs actively. The planning problem is formulated by mixed-integer programming. The proposed model is applied to an industrial zone as an aggregate load. Numerical simulations exhibit the model's efficacy and scrutinize in-depth, the effect of a variety of factors on multi-carrier microgrid planning results, including the extents of the capital investment fund and loan in addition to demand response enabling technology cost.

Off-Grid Multi-Carrier Microgrid Design Optimisation: The Case of Rakiura–Stewart Island, Aotearoa–New Zealand

The establishment of the concept of sustainable, decentralised, multi-carrier energy systems, together with the declining costs of renewable energy technologies, has proposed changes in off-grid electrification interventions towards the development of integrated energy systems. Notwithstanding the potential benefits, the optimal capacity planning of such systems with multiple energy carriers—electricity, heating, cooling, hydrogen, biogas—is exceedingly complex due to the concurrent goals and interrelated constraints that must be relaxed. To this end, this paper puts forward an innovative new optimal capacity planning method for a first-of-its-kind stand-alone multiple energy carrier microgrid (MECM) serving the electricity, hot water, and transportation fuel demands of remote communities. The proposed off-grid MECM system is equipped with solar photovoltaic panels, wind turbines, a hydrogen-based energy storage system—including an electrolyser, a hydrogen reservoir, and a fuel cell—a hybrid super-capacitor/battery energy storage system, a hot water storage tank, a heat exchanger, an inline electric heater, a hydrogen refuelling station, and some power converters. The main objective of calculating the optimal size of the conceptualised isolated MECM’s components through minimising the associated lifetime costs is fulfilled by a specifically developed meta-heuristic-based solution algorithm subject to a set of operational and planning constraints. To evaluate the utility and effectiveness of the proposed method, as well as the technical feasibility and economic viability of the suggested grid-independent MECM layout, a numerical case study was carried out for Rakiura–Stewart Island, Aotearoa–New Zealand. Notably, the numeric simulation results highlight that the optimal solution presents a low-risk, high-yield investment opportunity, which is able to save the diesel-dependent community a significant 54% in electricity costs (including electrified space heating)—if financed as a community renewable energy project—apart from providing a cost-effective and resilient platform to serve the hot water and transportation fuel needs.

Eco-Emission Analysis of Multi-Carrier Microgrid Integrated with Compressed Air and Power-to-Gas Energy Storage Technologies

Growing concerns about global greenhouse gas emissions have led power systems to utilize clean and highly efficient resources. In the meantime, renewable energy plays a vital role in energy prospects worldwide. However, the random nature of these resources has increased the demand for energy storage systems. On the other hand, due to the higher efficiency of multi-energy systems compared to single-energy systems, the development of such systems, which are based on different types of energy carriers, will be more attractive for the utilities. Thus, this paper represents a multi-objective assessment for the operation of a multi-carrier microgrid (MCMG) in the presence of high-efficiency technologies comprising compressed air energy storage (CAES) and power-to-gas (P2G) systems. The objective of the model is to minimize the operation cost and environmental pollution. CAES has a simple-cycle mode operation besides the charging and discharging modes to provide more flexibility in the system. Furthermore, the demand response program is employed in the model to mitigate the peaks. The proposed system participates in both electricity and gas markets to supply the energy requirements. The weighted sum approach and fuzzy-based decision-making are employed to compromise the optimum solutions for conflicting objective functions. The multi-objective model is examined on a sample system, and the results for different cases are discussed. The results show that coupling CAES and P2G systems mitigate the wind power curtailment and minimize the cost and pollution up to 14.2% and 9.6%, respectively.

A Scenario-Based Stochastic Model for Day-Ahead Energy Management of a Multi-Carrier Microgrid Considering Uncertainty of Electric Vehicles

A Scenario-Based Stochastic Model for Day-Ahead Energy Management of a Multi-Carrier Microgrid Considering Uncertainty of Electric Vehicles

Economic and Environmental Policy Analysis for Emission-Neutral Multi-Carrier Microgrid Deployment

Energy and global climate change crises are correlated issues since energy generation currently contributes to nearly 40% of global greenhouse gas emissions, and inevitably the escalating energy demand realization exacerbates the ongoing undesirable circumstances. As a viable solution against the concerns above, microgrid deployments with low-emission on-site resources have been receiving increasing attention from power decision-makers. Thus, this study scrutinizes the technical and financial sustainability of deploying a net-zero emission multi-carrier microgrid and determines the optimal generation configurations. Besides, the proposed emission-neutral multi-carrier microgrid model provides insights into the economic prominence of renewable energy incentives together with regulations for the short-term deployment of green resources. The objective of the proposed model is to minimize the multi-carrier microgrid deployment costs associated with the investment, operation, maintenance, energy demand shifting, monthly peak demand charge, emission, and reliability. Furthermore, load prioritization and a novel demand shifting of demand response schemes are propounded to maintain at least the continuous flow of energy for high-prioritized loads. The customer multi-carrier microgrid deployment problem is disintegrated into an investment master problem and an operation subproblem. The results indicate that notable electrical peak mitigation of about 7–23% is procured by employing the demand response scheme. Furthermore, the sensitivity analysis illustrates that renewable penetration of 30% along with dispatchable resources is required to procure the targeted share of green resources within microgrids. Finally, the economic and environmental merits of the proposed emission-neutral multi-carrier microgrid are ensured with savings and the discounted payback period of 131% and 3.977 years, respectively.

Dynamic Multi-Carrier Microgrid Deployment Under Uncertainty

An accurate assessment of microgrid economic benefits is a challenging task due to various uncertain data involved in the assessment. This paper scrutinizes the economic viability of multi-carrier microgrid deployments under various uncertainties, incorporating smart grid technologies, namely dispatchable and nondispatchable resources, energy storage devices, and demand response programs. The optimal type, size, and commissioning year of the available distributed energy resources along with economic utilization of selected resources are assessed to be implemented in the proposed multi-carrier microgrid. In this study, the stochastic multi-objective function comprises the net present value costs associated with the multi-carrier microgrid investment and installation, operation, maintenance, emission, incentive and penalty payments, and contracted power between multi-carrier microgrid owner and local utility company. Furthermore, an economic model for time-based demand response programs along with voluntary and mandatory demand response programs is developed innovatively. It correlates the local energy price of elastic loads for electrical and thermal carriers with day-ahead hourly pricing, energy import amounts, and on-site generations. The proposed dynamic planning problem under uncertainties of electrical and thermal loads, electricity price, and solar irradiation is solved by using a two-stage stochastic programming method that combines the genetic algorithm of MATLAB and the mixed-integer nonlinear programming model of GAMS software. Numerical simulation analyses demonstrate the effectiveness of the proposed multi-carrier microgrid expansion planning problem from the economic, environmental, and technical points of view.

Day‐ahead energy management framework for a networked gas–heat–electricity microgrid

The integration of various energy supplying systems increases the energy efficiency and energy system reliability. Smart microgrids are an ideal area to use multi-carrier energy systems. In this context, this study presents a novel modelling framework for optimal day-ahead scheduling of a networked multi-carrier energy microgrid (NMCEMG) system. The NMCEMG system in this study is composed of heat, gas, and power supply networks. The presented framework has enhanced the multi-carrier microgrid modelling against the previous works that modelled the multi-carrier microgrid as an energy hub due to difficulties in the energy flow analysis of heat and gas networks. The proposed framework optimises the day-ahead operating cost of the NMCEMG system considering nodal and energy flow constraints of each network. The proposed microgrid includes various energy interdependent equipment such as combined heat and power units, gas-fired boilers, power to gas units, electrical and heat storages and electric heat pumps. This study presents a new optimisation algorithm named self-adaptive modified whale optimisation algorithm based on wavelet theory to solve the day-ahead optimal scheduling of the NMCEMG problem. The numerical results corroborate the proposed modelling framework as superior over conventional hub-based multi-carrier microgrid models in terms of energy system security.

Reliability‐constrained optimal design of multicarrier microgrid

The unprecedented penetration of distributed generation in distribution energy networks provides utilities with a unique opportunity to manage portions of networks as microgrids (MGs). The implementation of an MG may offer many benefits, such as capital investments deferral, reduction of greenhouse gas emissions, improvement in reliability, and reduction in network losses. Future energy networks will contain various forms of energy which are acquired by mixing several sources and energy storages in the concept called multicarrier microgrid (MCMG). In order to draw the most effective performance from MCMG systems, appropriate design and operation are essential. This paper represents a compound co-optimization strategy to find the best type and size of components and the associated optimum dispatch in a grid-tied community MCMG implementing reliability criteria. Here, the required level of reliability is handled within the optimization process to fulfill multiple demands. The mixed-integer nonlinear programming (MINLP) technique of general algebraic modeling system (GAMS) and the genetic algorithm of MATLAB software are utilized to solve the co-optimization problem. Additionally, a contemporary time-based demand response program is modeled to reshape the load curve, as well as prevent the undue use of energy in peak hours. Eventually, the proposed strategy is applied to a test case to select the best components while respecting reliability restrictions. Numerical simulations prove the effectiveness of the proposed expansion planning.

Optimal sizing and techno-economic analysis of energy- and cost-efficient standalone multi-carrier microgrid

The objective of this study is optimal sizing and techno-economic analysis of an energy- and cost-efficient standalone multi-carrier microgrid (SMCMG). This SMCMG is with the integration of photovoltaic-thermal (PVT), wind turbine (WT), micro-turbine (MT), electrical energy storage (EES), thermal energy storage (TES), and backup natural gas boiler (GB) units. For this aim, a long-term planning approach is put forward, taking into account battery degradation and reliability considerations. Total annual cost (TAC) is served as the objective function of the optimization problem, which covers investment, replacement, fuel, and operation and maintenance costs. A rule-based energy management system (EMS), which prioritizes the application of renewable energy (RE) resources, is introduced to administrate the SMCMG operation and maximize RE utilization. To solve the optimization problem, a robust metaheuristic optimization algorithm, called evolutionary particle swarm optimization (E-PSO), is employed, and the results are validated. The simulations are conducted based on real data from Rafsanjan, Iran.The superiority of the E-PSO algorithm is demonstrated. Various techno-economic analyses are conducted, and different configurations for SMCMG are studied. The results signify the competence of the proposed configuration for SMCMG from economic, energy efficiency, reliability, and environmental points of view.