Multi-energy Microgrid Research Articles

Flexibility Exploitation With Nonlinear Integrated Demand Response for Multi-Energy System Against Load Estimation Mistake

Integrated demand response contributes to flexibility improvement in multi-energy system. Yet, the challenge posed by the load estimation mistake needs to be addressed to explore the available flexibility. In this paper, we present a price-based nonlinear integrated demand response for multi-energy microgrid against estimation mistakes of both electricity and gas loads. Among them, we develop a period partition method to describe load characteristics and reach a reasonable time-of-use pricing. Besides, a credibility theory-based risk measurement is presented to evaluate the uncertain wind power. Finally, case studies demonstrate the feasibility and effectiveness of the proposed method in flattening load curve, reducing operation cost and promoting the accommodation of wind power.

Regret-based multi-objective optimization of carbon capture facility in CHP-based microgrid with carbon dioxide cycling

Nowadays it is estimated that the Earth's temperature is above the standard level compared to pre-industrial time. The main reason for this issue is the burning of conventional energy resources namely oil, gas, and coal which speeds up global warming. In this concern, a CHP-based multi-energy microgrid is studied in this paper which involves heating, cooling, and electricity carriers to supply the energy demands. The proposed system is a multi-objective problem with operation cost management and emission cost reduction. In order to develop the environmental and economic aspects, a power-to-gas (P2G) technology as an environmentally friendly system is taken into account to curb CO2 emissions and achieve a sustainable function. The presence of the P2G system can minimize the emission level and improve the ability to take advantage of Notwithstanding this, the uncertain parameters originate from the thermal, electrical, and cooling loads, electricity price, and the renewable generations impose inflexibility and financial threats to the system function. In this regard, a scenario-based risk assessment approach, p-robust optimization, is applied to handle these uncertainties. The uncertainty analysis is carried out in With-p-robust, No-p-robust, With-P2G, and No–P2G models to appraise the components' performance. Results showed 11% of the operation cost increment to achieve a zero-level risk. Also, 55% of the emission cost is minimized in the presence of the P2G facility, which enhances the efficiency of the system.

Hybrid robust decentralized optimization of emission-aware multi-energy microgrids considering multiple uncertainties

Multi-energy microgrid (MEMG) can effectively coordinate various energy carriers and decarbonize the power system. Several MEMGs in the same distribution network are interconnected to form a multi-microgrid system, where a coordinator exists to coordinate these MEMGs to provide more cost-effective and reliable energy services. However, in this system, protecting each MEMG's information privacy is important. Moreover, the uncertainties caused by supply-demand sides and electricity price challenge the reliable operation of MEMGs. Therefore, this study proposes a hybrid robust decentralized energy management framework for the optimal day-ahead scheduling of interconnected MEMGs. Specifically, a decomposition strategy based on the alternating direction method of multipliers (ADMM) is utilized for privacy protection. The Wasserstein metric based distributionally robust optimization (DRO) and the robust optimization (RO) are incorporated to effectively deal with multiple uncertainties. Experimental results show that the coordination of multiple energy carriers (electricity, heat, hydrogen, and gas) can significantly lower the total cost and carbon emission cost (23.39% and 41.64%, respectively). Total cost can also be decreased by 3.15% with the implementation of integrate demand response (IDR). The used ADMM owns excellent performance, which can coverage to the result of centralized manner within a few iterations. Comparative analysis further demonstrated the predictability and superiority of the proposed hybrid robust optimization (HyRO).

Distributed tri-layer risk-averse stochastic game approach for energy trading among multi-energy microgrids

This paper discusses a tri-layer non-cooperative energy trading approach among multiple grid-tied multi-energy microgrids (MEMGs) in the restructured integrated energy market. The heterogeneous uncertainties from renewable energy, market prices, and electric energy loads are also considered via the risk-averse stochastic programming (SP) approach. First, comprehensive operation models of individual MEMGs are presented with the consideration of practical electric energy and thermal network flows as well as battery degradation. Second, to guarantee fair multi-energy trading among MEMGs and deal with adverse effects from all uncertainty sources, a tri-layer Cournot Nash game-based energy bidding method is developed and solved by the SP approach. In the first layer, i.e., day-ahead multi-energy market, optimal energy bids, dispatches of energy storage assets, and thermal flows against uncertainty scenarios are acquired in a risk-averse manner; In the second layer, i.e., intra-day multi-energy market, optimal intra-day energy bids and dispatches of all resources against uncertainty realizations are sequentially calculated; In the third layer, i.e., the real-time multi-energy market, transactions between each MEMG and the wholesale multi-energy market are finalized. Third, for protecting the privacy of individual MEMGs and alleviating the computation burdens, the distributed alternating search procedure is employed to compute the Nash equilibriums in the day-ahead and intra-day markets. In the end, numerical case studies are conducted to verify the effectiveness of our method. From the simulation results, it can be inferred that compared with the traditional cooperative, deterministic and risk-natural methods in the literature, our proposed method is more practical and economical for real-world applications since it comprehensively considers the market competition, uncertainty handling, and energy trading risk.

Configuration optimization of an off-grid multi-energy microgrid based on modified NSGA-II and order relation-TODIM considering uncertainties of renewable energy and load

This study develops a two-stage hybrid decision framework to configure an off-grid multi-energy microgrid (MEMG) while considering uncertainties in renewable energy resources and load demand. In the first stage, a non-dominated sorting genetic algorithm (NSGA)-II modified by reinforcement learning is employed to obtain Pareto solutions. Its three objectives are minimum total annual cost (TAC), loss of energy supply possibility (LESP), and energy abandonment rate (EAR). In the second stage, the weights for TAC, LESP, and EAR are obtained using the order relation method, and the best solution is chosen from Pareto solutions using a ranking method. Then, a case study is carried out to validate the rationality and feasibility of the proposed framework. The results show that when uncertainties and electricity storage are simultaneously considered, the respective values of TAC, LESP, and EAR are 81.148 million ¥, 16.146%, and 1.486%, respectively. A comparative analysis illustrates how the proposed method is better than NSGA-II, because the values of spacing metric and diversity are 0.0004 and 2.3546 obtained with the proposed method, and the former is smaller than 0.0013 and the latter is larger than 2.2713 obtained by employing NSGA-II. Therefore, the proposed framework is helpful to construct an off-grid renewable MEMG.

Optimal design and operation of a multi-energy microgrid using mixed-integer nonlinear programming: Impact of carbon cap and trade system and taxing on equipment selections

Multi-energy microgrids comprise various energy sources such as solar, wind, hydro, biomass, oil, gas, and coal. Optimal configuration and scheduling of multi-energy microgrids enhance energy efficiency and reduce carbon dioxide emissions. In this work, a novel and detailed mixed-integer nonlinear programming (MINLP) model is proposed to compare the effects of carbon emission taxing and cap and trade system on the optimal equipment selection and scheduling under the regulatory effects of the Paris Agreement. The candidate equipment to be selected by the MINLP model includes two solar panel arrays, two wind turbine farms, two integrated gasification combined cycles, two combined heat and power units, two conventional generators, one biogenerator, one battery, two bio-gasifiers, and one power-to-gas system. The impact of carbon dioxide price, taken as 50, 75, and 100 $/ton, is investigated for both emission taxing and carbon dioxide cap and trade system. Equipment selections with the multi-period seasonal data and the carbon dioxide prices of 50 $/ton and 75 $/ton in stand-alone mode are the same for both cases without selecting renewable sourced generators. For the same conditions and with a carbon dioxide price of 100 $/ton, the MINLP model selects one wind turbine farm, one biogenerator, and one bio-gasifier. Moreover, the power-to-gas system is chosen in electricity grid-connected mode under emission taxing with the carbon dioxide price of 100 $/ton. For both emission taxing and cap and trade system, the trials using the seasonal data and the yearly average data with carbon dioxide price of 75 $/ton in stand-alone mode contributes to different equipment selections. Results show that carbon dioxide price may be a crucial parameter for equipment selection and must be taken into account in the design phase for optimal design and management. Finally, the MINLP solvers SCIP, BARON, and DICOPT, are compared in terms of CPU times and equipment selections.

Multiple time-scale energy management strategy for a hydrogen-based multi-energy microgrid

With the technological advances in energy conversion and utilization of multiple forms of energy sources, hydrogen energy has attracted increasing attention. The hydrogen energy can be used to directly supply the hydrogen demand and generate both electricity and heat through fuel cell-based combined heat and power (FC-CHP) unit. This paper proposed a multiple time-scale energy management solution for a hydrogen-based multi-energy microgrid (MEMG) to supply electricity, hydrogen and heating loads aiming to minimize the MEMG operational cost with consideration of renewable energy generation and demand uncertainties. The proposed solution consists of day-ahead energy scheduling and model predictive control (MPC) based real-time energy dispatch in the presence of the electricity market. In the hydrogen-based MEMG, the electricity and hydrogen can be dispatched and utilized across multiple interconnected subsystems to improve the overall system energy utilization efficiency. The proposed solution is extensively assessed through simulation experiments compared with a benchmark solution. The numerical results confirm that the proposed solution outperforms the benchmark solution with the mean daily actual operational costs reduced by 37.08%.

Joint chance-constrained multi-objective optimal function of multi-energy microgrid containing energy storages and carbon recycling system

Nowadays, the emission of CO 2 has become a serious issue around the globe, upon which the Earth's temperature has dramatically risen. In this concern, a multi-energy CHP-based microgrid (MG) is considered to meet the multi-energy energy smart cities. As many generation units emit carbon dioxide, a power-to-gas (P2G) facility is considered in this paper to capture CO 2 emissions, upon which the environmental and economic perspectives of the system can be developed. The captured carbon dioxide can be used inside the CHP-based MG or sold to the natural gas network. Additionally, the presence of uncertainties in the loads, renewable energy resources (wind and PV), and electricity market potentially affect the flexibility and optimal function of the system, for which the joint chance constraint (JCC) approach as a scenario-based optimization method is applied for assessment of uncertain parameters. Based on the results, using the P2G facility can increase nearly 10 % of operation costs, while the emission is reduced by 50 %. Also, the applied JCC approach leads to reach the increased cost by 25 %, which shown the conservativeness behavior of the system's operator.

Preventive scheduling of a multi-energy microgrid with mobile energy storage to enhance the resiliency of the system

In multi energy microgrids with renewable energy sources, the significance of consideration of the gas supply network and efficient interaction between various energy carriers has been developed. Also, one of the most consequential matters linked with the operation of microgrids is affording the resiliency of the microgrid in inconstant conditions such as natural disasters. In this paper, the process of estimating resiliency in multi-energy microgrids is presented and the application of mobile energy providers is believed to prevent the devastating outcomes of natural disasters and improve the resiliency of the system. The methodology of this article is based on cost of not supplied energy for devastating effects of natural disasters on multi energy microgrids. Several scenarios have been presented to model the destructive consequences of natural catastrophes in the multi-energy microgrid. Different energy providers have been investigated to obtain the most desirable solution according to geographical conditions and demand characteristics. One of the important tools adopted in the multi-energy system to compensate for the resiliency of the system is mobile energy providers. According to the simulation outcomes, the value of the resiliency criteria and the total cost are improved due to the different scheduling strategies based on the application of mobile energy providers.

Data-driven distributionally robust joint chance-constrained energy management for multi-energy microgrid

Multi-energy microgrid (MEMG) has the potential to improve the energy utilization efficiency. However, the uncertainty caused by distributed renewable energy resources brings an urgent need for multi-energy co-optimization to ensure secure operation. This paper focuses on the distributionally robust energy management problem for MEMG. Various flexible resources in different energy sectors are utilized for uncertainty mitigation, then, a data-driven Wasserstein distance-based distributionally robust joint chance-constrained (DRJCC) energy management model is proposed. To make the DRJCC model tractable, an optimized conditional value-at-risk (CVaR) approximation (OCA) formulation is proposed to transfer the joint chance-constrained model into a tractable form. Then, an iterative sequential convex optimization algorithm is tailored to reduce the solution conservatism by tuning OCA. Numerical result illustrates the effectiveness of the proposed model.

Optimal stochastic scheduling of plug-in electric vehicles as mobile energy storage systems for resilience enhancement of multi-agent multi-energy networked microgrids

This paper presents an optimal scheduling of plug-in electric vehicles (PEVs) as mobile power sources for enhancing the resilience of multi-agent systems (MAS) with networked multi-energy microgrids (MEMGs). In each MEMG, suppliers, storage, and consumers of energy carriers of power, heat, and hydrogen are taken into account under the uncertainties of intermittent nature of renewable units, power/heat demands, and parking time of PEVs. In the case of contingencies, the proposed algorithm supplies energy to the on-fault MEMGs from normal-operated grid-connected MEMGs, using mobile PEVs. The procedure of selecting PEVs to supply energy to the on-fault MEMGs is performed in three stages. Initially, both on-fault and normal-operated MEMGs inform the central energy management system (EMS) about the amount of required energy and the amount of available energy from existing PEVs. Further, central EMS prioritizes the MEMGs among networked MEMGs to supply the energy support to the on-fault islanded MEMG. Lastly, the chosen MEMGs select their available efficient PEVs to supply energy to the on-fault islanded MEMG. Considering two diverse faulty case studies, the proposed technique is investigated in a MAS with four networked MEMGs. Simulated results demonstrate that the proposed algorithm enhances the resilience of MEMGs (over 25%) even without a physical connection between the MEMGs.

A Local Electricity and Carbon Trading Method for Multi-Energy Microgrids Considering Cross-Chain Interaction.

The objective of this paper is to propose a local electricity and carbon trading method for interconnected multi-energy microgrids. A local electricity market and a local carbon market are established, allowing microgrids to trade electricity and carbon allowance within the microgrid network. Specifically, excessive electricity and carbon allowance of a microgrid can be shared with other microgrids that require them. A local electricity trading problem and a local carbon trading problem are formulated for multi-energy microgrids using the Nash bargaining theory. Each Nash bargaining problem can be decomposed into two subproblems, including an energy/carbon scheduling problem and a payment bargaining problem. By solving the subproblems of the Nash bargaining problems, the traded amounts of electricity/carbon allowance between microgrids and the corresponding payments will be determined. In addition, to enable secure information interactions and trading payments, we introduce an electricity blockchain and a carbon blockchain to record the trading data for microgrids. The novelty of the usage of the blockchain technology lies in using a notary mechanism-based cross-chain interaction method to achieve value transfer between blockchains. The simulation results show that the proposed local electricity and carbon trading method has great performance in lowering total payments and carbon emissions for microgrids.

Multi-Energy Microgrids Incorporating EV Integration: Optimal Design and Resilient Operation

There are numerous opportunities and challenges in integrating multiple energy sources, for example, electrical, heat, and electrified transportation. The operation of multi-energy sources needs to be coordinated and optimized to achieve maximum benefits and reliability. To address the electrical, thermal, and transportation electrification energy demands in a sustainable and environmentally friendly multi-energy microgrid, this paper presents a mixed integer linear optimization model that determines an optimized blend of energy sources (battery, combined heat and power units, thermal energy storage, gas boiler, and photovoltaic generators), size, and associated dispatch. The proposed energy management system seeks to minimize total annual expenses while simultaneously boosting system resilience during extended grid outages, based on an hourly electrical and thermal load profile. This approach has been tested in a hospital equipped with an EV charging station in Okinawa, Japan through several case studies. Following a M1/M2/c queuing model, the proposed grid-tied microgrid successfully integrates EVs into the system and assures continued and economic power supply even during grid failures in different weather conditions.

A stochastic agent-based cooperative scheduling model of a multi-vector microgrid including electricity, hydrogen, and gas sectors

With increasing hydrogen usage, hydrogen subsystem should be considered in the multi-energy chain and a model is required that assumes current energy infrastructures, while preserving independent energy subsystems. In this study, a stochastic agent-based model is introduced for the coordinated scheduling of multi-vector microgrids considering interactions between electricity, hydrogen, and gas agents. Power to hydrogen (P2H) through electrolysis, hydrogen to power (H2P) through fuel cells, hydrogen to gas (H2G) through methanation, and gas to power (G2P) through distributed generation (DG) units are modeled to present the interactions among energy agents. The interactions in terms of shared variables and coupling constraints are described using augmented Lagrangian relaxation (ALR) and alternating direction method of multipliers (ADMM) to obtain three correlated optimization problems, preserving the privacy of energy sectors with minimum data exchange. An iterative process is accomplished among energy sectors to reach a consensus. Uncertainties in the wind turbine (WT) and photovoltaic (PV) power output, hydrogen vehicles (HVs), demands, and prices are captured using a stochastic method. To evaluate the proposed method, case studies are conducted using a multi-energy microgrid. The results verify that the microgrid is well scheduled and the interactions are accurately modeled, representing the effectiveness of the proposed method.

Hierarchical optimal configuration of multi-energy microgrids system considering energy management in electricity market environment

Aiming at the global environmental pressure and energy crisis, an optimal configuration method of a multi-energy microgrids system is proposed in the context of the electricity market. Firstly, the hierarchical collaborative optimization configuration framework of a multi-energy microgrid system is established. The upper-level regional energy supply is centrally coordinated and planned, and the lower-level multi-energy microgrids are independent and autonomous. At the same time, to reflect the electric energy interaction between various entities, a dynamic electricity price model between microgrids based on a multi-microgrid energy trading mechanism oriented by market supply and demand is established. To obtain the operation cost of the multi-energy microgrid, the microgrid operation strategy is analyzed under the cooperative operation of microgrids. Then, considering the interaction of the source-storage-load, a hierarchical collaborative optimal configuration model of the multi-energy microgrid group is established to minimize the economic cost. After that, the analytical target cascading method is adopted and a penalty function is introduced to satisfy the consistency constraints. Finally, a case study is conducted using the proposed approach. Experiments show that the optimal configuration based on the extreme scenarios and typical scenarios can reduce the total cost and maximize the interests of each subject. The economy of the system configuration is improved by 2 ∼ 3.5 %.

Decentralized P2P power trading mechanism for dynamic multi-energy microgrid groups based on priority matching

In order to promote the interaction among interconnected microgrids (MGs) with multiply distributed energy resources (DERs) to local energy utilization, a decentralized peer-to-peer (P2P) power trading mechanism based on priority matching is proposed. The priority indices are calculated based on scheduling results and quotations by an independent MG operator (IMO), which protect effectively the privacy and represent the bargaining willingness of MGs. According to the supply–demand ratio within a matching pair, each MG is permitted to update its quotation multi-times until the convergence or updated number limitation is reached. The clearing price of each pair is calculated by the mid-market rate (MMR) method. Because the imbalanced power is different with the autonomous scheduling in a MG at different dispatching time, the different power requirements of MGs results in the formation of the dynamic MG groups. The simulation results in an interconnected multi-microgrid system (IMMGS) with 4 MGs show the proposed decentralized P2P mechanism is reasonable and effective.

Two-stage resilience-constrained planning of coupled multi-energy microgrids in the presence of battery energy storages

In recent years, severe weather-related catastrophes have caused irreparable damages to the body of power systems, which has attracted the attention of planners to increase the resilience of power systems. In line with the goals of this issue, this paper introduces a two-stage programming on the basis of resilience oriented model to design solar panels, micro-turbines, and mobile batteries within the multi energy carrier microgrid to increase the resilience of the system against high-impact events. The propounded framework is formulated as mixed-integer quadratic programming that takes investment decisions at the first stage while optimizing operation variables at the second stage in order to reinforce the resilience of the system. An integrated power and gas model is used to increase the perception of readers about the mutual interactions between electricity and heat energy. Therefore, the availability of fuel and the price of the micro turbine are explicitly formulated and evaluated. One-line diagram of 14-bus coupled micro energy grids is used to investigate the efficiency and proficiency of the proposed model. Comparative studies numerically show that the suggested method helps the microgrid operator in identifying optimal solutions to reinforce the resilience of power system against severe events.

Research on the Evaluation of Multi-Energy Microgrid under the Background of New Power System

As a key means to elevate low-carbon energy transformation in China, multi-energy microgrid accelerates the construction of new power systems. In order to scientifically evaluate the benefits of multi-energy microgrids, we proposed a benefit evaluation index system from the dimensions of economy, reliability, low carbonization, and intelligence. Considering the relationship between the evaluation indicators, this paper innovatively proposed a multi-energy microgrid benefit evaluation model based on AHP-VWT-MEEM. In addition, this paper selected different multi-energy microgrid demonstration projects in the China Southern Power Grid Company Limited’s region for example analysis. In the example analysis, through the comparison of the comprehensive benefits of the three projects, the comprehensive benefit of the project 3 is the best. On this basis, from the specific configuration plan of Project 3, it can be concluded that the configuration of larger capacity thermal storage can realize the "thermal decoupling" of cogeneration, thereby improving the flexible adjustment capability of the demonstration area, promoting the consumption of renewable energy, and obtaining more considerable comprehensive benefits. At the same time, it is pointed out that the comprehensive benefit evaluation result can be improved by appropriately reducing the investment cost of project 3.

Distributionally robust co-optimized offering for transactive multi-energy microgrids

This paper proposes a two-stage distributionally robust co-optimization model for the microgrid offering problem. This model addresses the market participation of the microgrid including offering, market-clearing and reserve activation stages and captures the information availability from the market-side and the local distributed energy resource (DER) assets during the different stages. The DER assets are modeled through state-space models that capture the joint energy and contingency reserves provision capability for distributed generators, flexible building loads and battery storage systems. Moreover, uncertainties pertaining to market-clearing process, renewable generation, solar irradiation and ambient temperature are modeled in an ambiguity set that comprises statistical characterizations. Distributionally robust optimization (DRO) is employed to generate robust, but less conservative solutions. Linear decision rules and the second-order conic reformulations are employed to ensure the computational tractability of the DRO problem. The suitability of the proposed model is demonstrated for a synthetic multi-phase microgrid with spot market data from Singapore.

Battery storage configuration for multi-energy microgrid considering primary frequency regulation and demand response

In this paper, we study the optimal configuration problem of battery energy storage (BES) for multi-energy microgrid (MEMG) in two typical modes, which considers demand response in grid-connected mode and primary frequency regulation in islanding mode. In order to maximize the function of BES in the two modes, we first present the optimization model of MEMG with demand response in grid-connected mode to make profit. Then, considering the reliability and the power quality issues of MEMG, the BES should be used to participate primary frequency regulation to maintain the stability of the system frequency. Therefore, the constraint of primary frequency regulation in islanding mode is added to the optimization model. Computation results demonstrate that the proposed optimization strategy for BES capacity configuration can effectively reduce the operation cost and enhance the primary frequency regulation capability of the MEMG.