Multi-energy Microgrid Research Articles

Feasibility evaluation of a wind/P2G/SOFC/GT multi-energy microgrid system with synthetic fuel based on C-H-O elemental ternary analysis

Power to gas (P2G) uses electrical energy from access renewable power and captured carbon dioxide (CO2) to generate methane (CH4). The technology provides opportunity for replacing fossil fuels with green-powered hydrocarbon, benefiting the reducing of carbon emission. However, the methanation process in P2G requires high H2/CO2 ratio with available amount of hydrogen (H2) restricted by fluctuation of renewable power, bringing limits to the reusing of captured CO2. This paper presents a feasibility analysis of a novel wind/P2G/SOFC/GT multi-energy system (MES) for microgrid. Green-powered CH4 generated from P2G is mixed with captured CO2, bringing additional flexibility to balancing the overall H2/CO2 ratio for utilization. To comprehensively analyze the feasibility of synthesis CH4/CO2 fuel, evaluation of MES is carried out from both design and off-design conditions. For the design condition, a methodology of C-H-O elemental ternary analysis is applied to reflect the process of fuel utilization and reveal its connection with the trade-off feature of multiple components. For the off-design condition, fluctuations of user's load and renewable source during winter and summer scenarios are considered in a case study. Results show that under C-H-O distribution of 5.8 %, 61.2 % and 33.0 %, the SOFC/GT could operate safety with electrical efficiency of 62 %, capable of participating as a secondary power source for MES. Meanwhile, the overall H2/CO2 utilization ratio of the system is reduced from 4:1 to 2.4:1, where extremes conditions during winter and summer scenarios are evaluated with renewable penetration level of 94 % and wind curtailment rate below 5 % reached.

Optimal operation of multi-energy microgrids with shared energy storage clusters by master-slave games

Abstract As shared energy storage power plants have become a research hotspot, the cooperation between them and microgrids has attracted attention. In this paper, we propose a master-slave game optimization operation strategy for multi-energy microgrids, taking into account shared energy storage (ESS) clusters. Firstly, the inclusion of ESS clusters is considered in a combined heat and power (CHP) type multi-energy microgrid. Then, the optimal operation model is established and solved in two layers. The master-slave game model in the context of ESS clusters is proposed by analyzing the game relationship between the microgrid side and the user side, the arithmetic simulation with MATLAB, and the modeling and solving are carried out by the Yalmip tool and the CPLEX solver, and the combination of heuristic algorithms and the solver is used to optimize the strategies of the microgrid side and the user side. The optimization of the system after the optimization of this paper’s methodology can enhance the system economy.

Joint Planning Method of Shared Energy Storage and Multi-Energy Microgrids Based on Dynamic Game with Perfect Information

Under the background of the Energy Internet and the shared economy, it is of great significance to explore the collaborative planning strategies of multi-energy microgrids (MEMGs) and a shared energy storage operator (SESO) supported by shared energy storage resources. In this context, a joint planning method of SESO and MEMG alliances based on a dynamic game with perfect information is proposed in this paper. First, an upper-level model for energy storage capacity configuration and pricing strategy planning of SESO is proposed to maximize the total planning and operational income of SESO. Then, a lower-level model for the optimal configuration of MEMGs’ alliance considering SES is proposed to minimize the total planning and operational costs of the MEMG alliance. On this basis, a solving algorithm based on the dynamic game theory with perfect information and the backward induction method is proposed to obtain the Nash equilibrium solution of the proposed bi-level optimization models. Finally, a case study with one SESO and an alliance consisting of five MEMGs is conducted, and the simulation results show that the proposed bi-level optimization method can increase SESO’s net income by 1.47%, reduce the average planning costs for each MEMG at least by 1.7%, and reduce model solving time by 62.9% compared with other counterpart planning methods.

Eco-environmental regret-aware optimization of networked multi-energy microgrids with fully carbon elimination and electric vehicles' promotion

With the escalating concern of global warming propelled by the rise in Earth's temperature, the need for effective CO2 management has become crucial. This paper presents an innovative CO2 elimination approach, wherein a multiple integrated system of energies (MISEs) incorporating sustainable resources, including renewable resources (RENs), plug-in electric vehicles (PEVs), and demand response programs, is optimized. The proposed carbon elimination framework begins by modeling the onsite carbon capturing and recycling within each MISE. To effectively utilize the onsite carbon recycling facilities and achieve carbon neutrality, the proposed model also incorporates carbon transfer capability between MISEs, thereby enhancing the efficiency of overall carbon recycling. Furthermore, a stochastic p-robust optimization technique is proposed to effectively manage uncertainties by combining the advantages of stochastic programming and robust optimization. This uncertainty modeling approach promotes greater utilization of sustainable resources like PEVs and RENs due to their lower operational regrets from economic and environmental perspectives. Based on the simulation results, implementing the p-robust-based regret assessment technique led to the total operation cost increasing by only 2.75 %, while achieving a significant 44.5 % reduction in maximum relative regret. These results underscore the effectiveness of the proposed framework in enhancing both the economic and environmental performance of MISEs.

Optimizing integrated hydrogen technologies and demand response for sustainable multi-energy microgrids

Optimizing integrated hydrogen technologies and demand response for sustainable multi-energy microgrids

Collaborative optimization of multi-energy multi-microgrid system: A hierarchical trust-region multi-agent reinforcement learning approach

In the context of the expanding diversity of energy demands, an increasing number of heterogeneous Multi-energy Microgrids (MEMGs) are engaging in the collaborative framework of the Multi-energy Multi-microgrid System (MEMMG). However, following this trend, the existing centralized Integrated Energy Management System (IEMS) control strategy is unreliable for future energy systems, characterized by more complex optimization control and a flexible system structure. This paper introduces a hierarchical Multi-agent Deep Reinforcement Learning (HMADRL) approach for distributed IEMS in MEMMG. Firstly, by employing a hierarchical approach, this method simplifies control complexity by segmenting the overarching control challenge into tasks of collaborative planning and action control, which are distributed across varied multi-agent scenes. Considering both macro and microeconomic factors, alongside carbon emissions, the optimal operation of MEMMG is realized through a bottom-up edge multi-agent control approach, in contrast to traditional top-down centralized methods. Secondly, in the phase of the inter-MEMG collaborative strategy, the Centralized Training Decentralized Execution (CTDE) framework is adopted to overcome the problems of unstable training environments and large-scale agent training, and each heterogeneous microgrid can develop local strategies independently with the assurance that their internal data will not be overly exposed. Thirdly, within each MEMG, the Trust-Region (TR) model is introduced for multi-agent action control, adeptly addressing the effects of mutual exclusion in decision-making time series. Simultaneously, an initialized Hot Experience Pool (HEP) is implemented, efficiently reducing exploration in complex, high-dimensional spaces. Finally, the off-time agent model is integrated into the HMADRL environment and undergoes secondary training based on real interactions, thereby deriving the optimal energy management policy. The proposed method markedly reduces reliance on exact physical modeling systems. Comparative simulations validate the proposed control scheme’s efficacy.

Energy Management of Green Port Multi-Energy Microgrid Based on Fuzzy Logic Control

The green port multi-energy microgrid, featuring renewable energy generation, hydrogen energy, and energy storage systems, is an important gateway to achieve the net-zero emission goal. But there are many forms of energy in green port multi-energy microgrid systems, the power fluctuates frequently, and the port loads with large fluctuations and fast changes. These factors can easily lead to the problem of the state of charge exceeding the limit of the energy storage system. To distribute the fluctuating power in the green port multi-energy microgrid system reasonably and maintain the state of charge (SOC) of the hybrid energy storage system in an moderate range, an energy management strategy (EMS) based on dual-stage fuzzy control with a low pass-filter algorithm is proposed in this paper. First, the mathematical model of a green port multi-energy microgrid system is established. Then, fuzzy rules are designed, and the dual-stage fuzzy controller is used to change the time constant of the low-pass filter (LPF) and modify the initial power distribution by an LPF algorithm. Finally, simulation models are built in Matlab 2016a/Simulink. The simulation results demonstrate that, compared with other algorithms under the control of the EMS proposed in this paper, the high-frequency component in the flywheel power is smaller, and the SOC of the supercapacitor is maintained in a reasonable range of 34–78%, which extends the lifespan of the flywheel and supercapacitor. Additionally, it has a faster automatic adjustment ability for the state of charge of the energy storage system, which is conducive to better maintaining the stable operation of green port multi-energy microgrid systems.

Low‐carbon economic operation of multi‐energy microgrid based on multi‐level robust optimisation

AbstractThe economic and low‐carbon operation strategy of multi‐energy microgrids (MEM) has become an important research topic in smart grids. The operation of MEM is affected by uncertain factors from renewable energy and internal load. To handle uncertainties from both source and load sides, the authors propose a novel worst‐case‐and‐probability uncertainty sets and a novel worst‐expectation min‐max‐max‐min two‐stage four‐level robust optimisation (RO) model considering stepped carbon trading for MEM. This four‐level RO model is a more generalised model compared with the traditional two‐stage RO and distributionally robust optimisation based models. First, the unsolvable original four‐level model is decoupled into a master problem and a sub‐problem (SP). Second, Karush–Kuhn–Tucker condition is applied to convert SP into a solvable problem. Third, column and constraint generation (C&CG) algorithm is employed to solve the reformulated four‐level RO. Finally, the effectiveness of the proposed model and solution method is verified by case studies. The results indicate that the convergence behaviour of this solution method is excellent. The MEM operator can select appropriate robustness factors and comprehensive norms to control its conservatism level. Besides, MEM could reduce carbon emissions by participating in carbon trading and installing carbon capture devices.

Application of day-ahead optimal scheduling model based on multi-energy micro-grids with uncertainty in wind and solar energy and energy storage station

Application of day-ahead optimal scheduling model based on multi-energy micro-grids with uncertainty in wind and solar energy and energy storage station

Two-stage robust operation of electricity-gas-heat integrated multi-energy microgrids considering heterogeneous uncertainties

With the widespread adoption of combined heat and power and power-to-heat technologies, multi-energy microgrids (MEMGs) have been garnering significant research attention from both industry and academia. However, dealing with uncertainties from renewable energy and load and coordinating multiple energy carriers are the main challenges for MEMG operation. In this regard, a two-stage robust operation method of electricity-gas-heat integrated MEMGs considering heterogeneous uncertainties is proposed in this paper. First, network models for an electricity-gas-heat-based distribution-level MEMG are formulated considering the dynamic characteristics of gas and heat networks. Then, the power-to‑hydrogen-and-heat unit and ladder-type carbon trading mechanism are introduced to reduce the curtailment of wind power and carbon emissions. Further, a two-stage robust optimization (TSRO) method is applied to tackle uncertainties of wind power and load under extreme scenarios in the MEMG operation by iteratively solving the operation problem with the column and constraint generation (C&CG) algorithm. Finally, case studies are conducted to verify our proposed method, demonstrating that it can reduce the multi-energy supply cost while the stepped carbon trading mechanism can also significantly reduce carbon emissions.

Prediction and scheduling of multi-energy microgrid based on BiGRU self-attention mechanism and LQPSO

To predict renewable energy sources such as solar power in microgrids more accurately, a hybrid power prediction method is presented in this paper. First, the self-attention mechanism is introduced based on a bidirectional gated recurrent neural network (BiGRU) to explore the time-series characteristics of solar power output and consider the influence of different time nodes on the prediction results. Subsequently, an improved quantum particle swarm optimization (QPSO) algorithm is proposed to optimize the hyperparameters of the combined prediction model. The final proposed LQPSO-BiGRU-self-attention hybrid model can predict solar power more effectively. In addition, considering the coordinated utilization of various energy sources such as electricity, hydrogen, and renewable energy, a multi-objective optimization model that considers both economic and environmental costs was constructed. A two-stage adaptive multi- objective quantum particle swarm optimization algorithm aided by a Lévy flight, named MO-LQPSO, was proposed for the comprehensive optimal scheduling of a multi-energy microgrid system. This algorithm effectively balances the global and local search capabilities and enhances the solution of complex nonlinear problems. The effectiveness and superiority of the proposed scheme are verified through comparative simulations.

Industrial multi-energy communities as grid-connected microgrids: Understanding the role of asymmetric grid-charge regulation

The industrial sector is currently the leading emitter of greenhouse gases worldwide. Lowering emissions, the collaborative use of energy and storage technologies in Industrial Energy Communities (IEC) is a promising option, typically implemented as a grid-connected microgrid. To support successful implementations of IECs, it is essential to understand not only the interaction of different technical assets within an IEC but also the corresponding regulation that determines the IEC's economic and ecological performance. Similar to different technical capabilities of available assets, companies of an IEC are typically affected by regulation in different, asymmetric ways. To the best of our knowledge, we are the first to investigate the economic and ecologic effects stemming from asymmetric regulation, i.e., regulation that differs between different participating companies via a microgrid approach. By developing a novel linear model for German asymmetric grid charge regulation, we are able to optimize the economic operation of complex multi-energy microgrids under detailed regulatory conditions. In more detail, we formulate and implement a mixed-integer linear program to investigate the joint operation of a multi-energy IEC under asymmetric regulation. We conduct a real-world case study to evaluate the effects of German grid-charge regulation as a significant example of asymmetric regulation and compare the results of our IEC to a situation where every company of the IEC manages its assets individually. Our results indicate that IECs have the potential to significantly reduce the total operational energy costs under the current asymmetric German grid-charge regulation. While the shared assets see a higher utilization in the IEC, the impact on emissions is, however, limited.

Online EVs Vehicle-to-Grid Scheduling Coordinated with Multi-Energy Microgrids: A Deep Reinforcement Learning-Based Approach

The integration of electric vehicles (EVs) into vehicle-to-grid (V2G) scheduling offers a promising opportunity to enhance the profitability of multi-energy microgrid operators (MMOs). MMOs aim to maximize their total profits by coordinating V2G scheduling and multi-energy flexible loads of end-users while adhering to operational constraints. However, scheduling V2G strategies online poses challenges due to uncertainties such as electricity prices and EV arrival/departure patterns. To address this, we propose an online V2G scheduling framework based on deep reinforcement learning (DRL) to optimize EV battery utilization in microgrids with different energy sources. Firstly, our approach proposes an online scheduling model that integrates the management of V2G and multi-energy flexible demands, modeled as a Markov Decision Process (MDP) with an unknown transition. Secondly, a DRL-based Soft Actor-Critic (SAC) algorithm is utilized to efficiently train neural networks and dynamically schedule EV charging and discharging activities in response to real-time grid conditions and energy demand patterns. Extensive simulations are conducted in case studies to testify to the effectiveness of our proposed approach. The overall results validate the efficacy of the DRL-based online V2G scheduling framework, highlighting its potential to drive profitability and sustainability in multi-energy microgrid operations.

Collaborative Optimization Scheduling of Multi-Microgrids Incorporating Hydrogen-Doped Natural Gas and P2G–CCS Coupling under Carbon Trading and Carbon Emission Constraints

In the context of “dual carbon”, restrictions on carbon emissions have attracted widespread attention from researchers. In order to solve the issue of the insufficient exploration of the synergistic emission reduction effects of various low-carbon policies and technologies applied to multiple microgrids, we propose a multi-microgrid electricity cooperation optimization scheduling strategy based on stepped carbon trading, a hydrogen-doped natural gas system and P2G–CCS coupled operation. Firstly, a multi-energy microgrid model is developed, coupled with hydrogen-doped natural gas system and P2G–CCS, and then carbon trading and a carbon emission restriction mechanism are introduced. Based on this, a model for multi-microgrid electricity cooperation is established. Secondly, design optimization strategies for solving the model are divided into the day-ahead stage and the intraday stage. In the day-ahead stage, an improved alternating direction multiplier method is used to distribute the model to minimize the cooperative costs of multiple microgrids. In the intraday stage, based on the day-ahead scheduling results, an intraday scheduling model is established and a rolling optimization strategy to adjust the output of microgrid equipment and energy purchases is adopted, which reduces the impact of uncertainties in new energy output and load forecasting and improves the economic and low-carbon operation of multiple microgrids. Setting up different scenarios for experimental validation demonstrates the effectiveness of the introduced low-carbon policies and technologies as well as the effectiveness of their synergistic interaction.

Reliability-based sizing of islanded multi-energy microgrid: a conic chance-constrained optimization approach

AbstractThis paper presents a methodology to optimally design a multi-energy microgrid with thermal and electric loads considering $$N-1$$ N - 1 and probabilistic regulation reserves. This methodology consists of a chance-constrained optimization that determines the optimal sizing of the microgrid. Microgrid operations are rigorously considered by modelling hourly thermal and electric demand patterns as well as technology production schedules over a year. Such schedules include both electric and thermal power balances, ramp constraints, $$N-1$$ N - 1 and regulation reserves, among others. To ensure a reliable microgrid design and operation, reserve constraints have been proposed to deal with both $$N-1$$ N - 1 generation contingencies, and forecast errors. $$N-1$$ N - 1 reserves guarantee that a sudden outage of any of the electric generators is strategically covered by the remaining generators in order to avoid load shedding. Additionally, nonzero-mean random forecast errors of electric load and solar production are addressed by a set of chance constraints able to schedule asymmetric up and down regulation reserves. Their levels are high enough to cover hourly random forecast errors (or intermittencies) with a high threshold probability. The proposed methodology results in a mixed integer second-order cone program. Results of microgrid designs with and without reliability reserves are carefully analysed and compared. Neglecting reliability constraints leads to a lower-cost design at the expense of exposing the microgrid to unsafe operation. Finally, sensitivity analysis to study the optimal portfolio sizing with respect to electric BESS investment cost, solar production forecast mean error, and random intermittency threshold probability are performed.

Optimal sizing of multi-energy microgrid with electric vehicle integration: Considering carbon emission and resilience load

To address the intermittency of renewable energy sources, global warming, and increasing load demands, this paper proposes the optimal sizing of a multi-energy microgrid (MEMG) consisting of electrical, thermal, cooling, and hydrogen networks. The system integrates multi-energy storage and EVs along with the resilience load to facilitate a robust operation scheme. The paper introduces an improved resilience backup mechanism for EVs using hybrid storage. Then, random samples for stochastic parameters are generated with Monte Carlo Simulations (MCS). To this end, a mixed-integer linear programming-based model is developed to minimize cost, emissions, and load shed. Then, a CPLEX solver is applied to solve it efficiently. The optimal MEMG with the hydrogen network reduces cost by 4% and emissions by 40%. The case study validates that hybrid storage (BES-HST-TST) can effectively reduce the electricity grid purchase to zero, making MEMG self-sufficient while yielding the least annual system cost (ASC) of 3855562$ and decreasing emissions to 8385 kg, resulting in economic savings, environmental sustainability, and increased utilization of renewables. Notably, V2G can save 0.7546% of MEMG extra cost incurred due to EVs integration and significantly reduces CO2 emissions by 4%. A novel finding is that the proposed hybrid storage backup mechanism can effectively minimize the extra cost of keeping the resilience load by retaining 31.46% of backup in hydrogen form. It mitigates risk associated with power outages while achieving the least ASC of 3858933$ and CO2 emissions of 8043 kg. These results can help realize a green, cost-effective, and efficient energy system.

Coordinating Multi-Energy Microgrids for Integrated Energy System Resilience: A Multi-Task Learning Approach

Coordinating Multi-Energy Microgrids for Integrated Energy System Resilience: A Multi-Task Learning Approach

A Multi-Objective Stochastic Optimization Approach for Planning a Multi-Energy Microgrid Considering Unscheduled Islanded Operation

A Multi-Objective Stochastic Optimization Approach for Planning a Multi-Energy Microgrid Considering Unscheduled Islanded Operation

Distributed optimization for joint peer‐to‐peer electricity and carbon trading among multi‐energy microgrids considering renewable generation uncertainty

AbstractThe increasing penetration of renewable energy and the further coupling of the electricity and carbon markets have hindered the realization of efficient and low‐carbon transformation processes in new power systems. This study addresses the optimization problems of joint peer‐to‐peer (P2P) electricity and carbon trading in multi‐energy microgrids (MEMGs), taking into account the risks associated with renewable generation in a distributed manner. First, a coordinated operation model is developed to describe the joint P2P electricity and carbon trading issues among MEMGs, aiming to minimize operating costs, mitigate potential risk losses, and reduce renewable energy wastage. Second, the conditional value‐at‐risk technique, paired with stochastic programming, is employed to quantify potential risk losses arising from uncertainties. Finally, a distributed optimization approach is developed based on the alternating direction method of multipliers to maintain the privacy and independence of decision‐making in individual MEMGs. During the trading processes, the Lagrangian multipliers are used as price signals to ensure fairness in optimal trading schemes among MEMGs. Moreover, a parallel solution mechanism is implemented to improve overall operational efficiency with minimal calculation expenditure. The simulation results demonstrate that the proposed method can reduce operation costs and carbon emissions while also preventing a significant amount of renewable energy abandonment.

Deep learning-based solar power forecasting model to analyze a multi-energy microgrid energy system

Multi-energy microgrids (MEM) are a new class of power grids focusing on the distributed form of generation and integrating different energy sectors. The primary idea of MEM is to increase renewable energy share in the final energy demand while maintaining the energy balance at all times. However, integrating renewable technology into the grid has some technical limitations that must be analyzed before being deployed in the real world. This study examines the impact of increasing renewable penetration and portfolio design on a multi-energy microgrid energy system from a technical standpoint. As the accuracy of the system analysis is primarily a factor of modeling accuracy, an artificial neural network-based model is trained and deployed to develop forecasts for solar power generation. The forecasting model is integrated with the EnergyPLAN simulation tool to analyze the multi-energy microgrid system regarding renewable share in primary energy consumption and import/export of energy from the primary grid. The Norwegian energy system is considered a case study, as the energy generation and consumption patterns are interesting from both renewable energy and demand contexts for a cold country. One interesting conclusion is that the portfolio and capacities of coupling components such as combined heat and power plants negatively impact renewable integration, while heat pumps positively impact renewable integration by increasing renewable energy utilization. Additionally, the photovoltaic system size has a high degree of correlation to imports and exports compared to wind generation systems.