Multi-energy Demand Research Articles

Multi-swarm multi-tasking ensemble learning for multi-energy demand prediction

The increasing uptake of renewable energy sources leads to more uncertainties of energy scheduling, resulting in more difficulties of energy demand management in smart grid. Accurate forecasting plays a significant role in energy management, and in some cases it may involve predictions of different consumers or geographic regions. Traditionally, these prediction problems have been solved independently, ignoring the potential shared knowledge among them that may help facilitate the overall performance. In this manuscript, we propose a multi-swarm multi-tasking ensemble learning (MSMTEL) framework for solving the energy demand forecasting across multiple cities. The proposed method comprises single-task pretraining, multi-task optimization (MTO), and ensemble learning (EL). For each prediction task, several subtasks are generated based on predefined parameters in the forecasting model. Each subtask utilizes an independent deep neural network (DNN) as the predictor, which is pretrained individually. Subsequently, we modify the dynamic multi-swarm particle swarm optimization (DMS-PSO) as a customized multi-swarm PSO (CMS-PSO) algorithm for implementing MTO. Each subswarm in CMS-PSO focuses on finding the optimal model knowledge (pretrained DNN weights and biases) of source tasks (all subtasks) to be reused by the target subtask. Finally, instead of choosing the best forecasting among the subtasks, results of subtasks over the same energy prediction problem are combined to yield the EL result, wherein weight coefficients determining the contributions of subtasks to EL are optimized by PSO. MSMTEL is evaluated against single-task learning (STL) and MTO to demonstrate its superior performance.

Optimal scheduling of a community Multi-Energy system in energy and flexible ramp markets considering Vector-Coupling storage Devices: A hybrid Fuzzy-IGDT/Stochastic/Robust optimization framework

Community multi-energy systems (CMESs) are emerging as a pivotal solution to address the pressing issues of the current energy crisis and environmental pollution. This paper introduces a comprehensive multi-horizon risk-averse scheduling strategy for CMESs, targeting participation in energy and flexible ramp markets while meeting the demands of electric, natural gas, and hydrogen vehicle charging stations, data centers, and residential and commercial buildings. The proposed CMES incorporates power-to-x vector coupling storage (PtX-VCS) systems, enabling the storage of electricity in various forms to cater to multi-energy demands. Additionally, an integrated demand response (IDR) program, incorporating incentive and load-shifting strategies, is applied to enhance operational efficiency and system flexibility. To manage uncertainties effectively, a hybrid stochastic/robust/fuzzy information gap decision theory (IGDT) method is employed. This approach, formulated as a tri-level optimization model, is transformed into a single-level model using strong duality theory. The results demonstrate a notable reduction in daily operational costs by approximately 5% and 4% through the implementation of the IDR program and PtX-VCS technologies, respectively. Moreover, the CMES’s potential to participate in the flexible ramping market leads to an approximately 7% reduction in total operational costs.

Optimal bidding strategy for multi-energy virtual power plant participating in coupled energy, frequency regulation and carbon trading markets

Multi-energy virtual power plant (MEVPP) with diversified flexible resources can participate in energy market (EM), frequency regulation market (FRM) and carbon trading market (CM) to obtain extra benefits. However, the complicated market environment provokes grant challenges in the bidding strategy of MEVPP that coordinates heterogenous supply-side devices and demand-side users. To the end, this study proposes a multi-objective bidding strategy for MEVPP comprising various energy flows and multi-energy demand response (MDR) in EM, FRM and CM. Results are delivered that: 1) Compared to the only profit-oriented optimization, the profit of comprise solution under the multi-objective declines by 46% but the satisfaction level improves 8%. Relative to the single satisfaction-oriented optimization, a 5.2% satisfaction loss allows for an approximately 103% growth in profit, realizing a win-win result for MEVPP and users. 2) There were negative profits in the pure EM (Case 1) as well as EM and CM (Case3). Compared with positive profit in EM and FRM (Case 2), the profit in coupled EM, FRM and CM (Case 4) increases by 48.9%. Relative to the CO2 emission in EM and CM, the emission under the proposed strategy mitigates by 9.36%. 3) A carbon base price at 200 CNY/t CO2 and an emission interval length of 5–10 t are recommended to maintain the low-carbon economic operation for MEVPP.

A Two-Stage Framework for Exploiting the Multi-Elasticity of Multi-Energy Demands in Integrated Electricity and Gas Markets

With the development of integrated electricity and gas markets (IEGM), the participation of multi-energy demands (MEDs) is an effective measure to achieve a win-win goal of market efficiency improvement and demand cost reduction. The MEDs, composed of terminal users and energy conversion equipment (ECE), can adjust purchased electricity and gas with the variation in energy prices. Such adjustment behaviors of purchased energy in response to energy prices are defined as the multi-elasticity of MEDs. Considering that, this paper aims to analyze and quantify the multi-elasticity of MEDs and exploit it using a market-based approach. Firstly, the volume-price bidding curves are proposed to describe the multi-elasticity of MEDs considering the price-response features of terminal loads and multi-energy substitution features of ECE. Based on the proposed bidding curves, the energy consumption behaviors of MEDs with the change in prices can be effectively quantified. Moreover, a decentralized two-stage market framework is proposed to exploit the multi-elasticity in the IEGM clearing based on the multi-elasticity bidding curves of MEDs. In the proposed framework, the electricity market and gas market are cleared separately with the information exchange of the MEDs' clearing results of purchased gas and electricity. Furthermore, the consensus ADMM-based algorithm is applied to coordinately solve the IEGM clearing problem integrated with the multi-elasticity of MEDs. Numerical simulations are carried out for different test systems to validate the effectiveness of the proposed models.

Stochastic power allocation of distributed tri-generation plants and energy storage units in a zero bus microgrid with electric vehicles and demand response

This work proposes a sequential stochastic coordinated energy management scheme (SCEMS) for a multi-energy carrier zero bus microgrid (ZBMG) in the presence of distributed tri-generation plants (TGPs), renewable sources, energy storage systems (ESSs), plug-in hybrid electric vehicles (PHEVs), auxiliary boiler and chiller units, and controllable loads. The first stage of the proposed architecture deals with implementing an integrated price-based demand response program to maximize consumers’ benefits and boost demand-side participation. The impacts of grid-to-vehicle and vehicle-to-grid modes of PHEV operation are investigated in this study. The second stage of this formulated problem considers the optimal power allocation of TGPs, ESS devices and auxiliary units aiming to satisfy economic, environmental, flexible, and reliability-driven objectives. Furthermore, to ensure optimal serving of heat and cooling load, novel heat and cool-following strategies are suggested. The uncertainties associated with the renewable generators, PHEV load, grid energy price, and load demands are modelled and incorporated in the SCEMS following “Hong’s 2m+1 point estimate method”. This stage also optimizes the distribution network structure for efficient microgrid operation. Moreover, a novel P−PQVδ−zero+ bus triplet approach of controlling power factor of dispatchable distributed generator is proposed. The efficacy of the framed problem is corroborated through examples. The simulation studies show that the proposed coordinated energy management strategy can: (1) economically accomplish peak shaving and valley filling for multi-energy demands; (2) reduce operation cost and electric energy loss by 8.19%∼10.38% and 41.63%∼47.91%, respectively; (3) enhance average flexibility index by 4.06%∼5.62% but with a marginal rise in emission by 0.39%∼1.68%; and (4) improve node voltage profile of the system.

Comprehensive multi-benefit planning of sustainable interconnected microgrids

A comprehensive stochastic planning model with multi-objective, integrated sizing and scheduling strategies for distributed energy resources is fabricated and implemented for interconnected grid-tied microgrids in the present work. The optimum capacities of the renewable and dispatchable distributed generators, together with battery energy storage systems in the interconnected microgrids, are ascertained using grey wolf optimizer in the fuzzy domain with the objectives of minimizing the yearly expenses, emissions and energy loss, and maximizing the yearly monetary benefit acquired due to the deferral of network upgrades. The optimal dispatch of multi-energy resources is intended to satisfy price-sensitive, multi-energy demands economically and sustainably under the influence of a price-based demand response strategy. The proposed multi-microgrid planning framework, which is hinged on probabilistic power flow with the probabilistic modelling of renewable and load uncertainties, is targeted to benefit multiple entities, including the utility and microgrid prosumers. The effectiveness of the proposed formulation is demonstrated by the mitigation of the annual expenses, emissions, and energy loss by 51%, 70%, and 56%, respectively, and the deferral of the need for the relevant upgrades by 12 years. Moreover, the performance of the integrated resource allocation and scheduling model is also endorsed using environmental sustainability factors.

Potential of applying the thermochemical recuperation in combined cooling, heating and power generation: Optimized recuperation regulation with syngas storage

In the traditional combined cooling, heating, and power (CCHP) systems, the frequently changed user loads result in unfavorable supply-demand matching characteristics, which weakens the practical operational regulation performance. In this work, the thermochemical recuperation (TCR) with syngas storage method is proposed to optimize the system regulation characteristics and enhance the waste heat recovery. Various building energy load scenarios are also adopted to comprehensively evaluate the modified CCHP system operation characterizes. Meanwhile, with the consideration of reasonable operation regulation of syngas storage and multi-energy demands supplementary between different buildings, the coordinated system energy scheduling capacity are deeply explored to investigate the practical system application feasibility. The results indicate that under the designated operating conditions, the cooling/heating-to-power output ratios have been adjusted from 2.45 and 2.73 to 0.24–2.55 and 0.64–2.85, respectively. Additionally, with the efficient operation regulation process of TCR and syngas storage, the annual average energy utilization efficiency of the hotel, hospital, office and shopping center will be increased by 3.88%, 3.85%, 4.50% and 3.98%. Besides, through the synergistic complementary of syngas storage and adjustment between shopping center and hospital, the annual average syngas storage utilization rate of the shopping center can be increased from 16.99% to 97.41%, with the annual fuel consumption reduction of 5.78% in hospital scenario. This paper provides a combined TCR and syngas storage regulation method for the CCHP system, which enhances the system energy output flexibility and waste heat recovery capabilities in the distributed energy system application.

Research on multi-energy collaborative operation optimization of integrated energy system considering carbon trading and demand response

Integrated energy systems (IES) have been applied to raise the efficiency of energy utilization and facilitate the sustainable transition of society and energy system. To further explore the multi-energy coupling capacity and carbon reduction potential of the IESs, this study proposes a cooling-heat-electricity-gas collaborative optimization model of IES given ladder carbon trading (LCT) mechanism and multi-energy demand response (MDR). To begin with, the MDR model is developed following the demand characteristics of the system. Given the incentive effect of the LCT on the low-carbon performance of the IESs, the multi-energy coupling low-carbon operation optimization model is proposed. By constructing four application scenarios, the proposed model is practiced in investigating the performance of IES across various market mechanisms. The simulation findings indicate the introduction of the LCT cuts the carbon emissions of the IES by approximately 6.7%, and the MDR mechanism effectively optimizes the energy use curve by regulating user behavior. The combined consideration of the two market mechanisms saves around 9.21% of the IES's operation cost and strengthens the performance of IES towards the green and economic operation. The carbon market requires rational prices to inspire IES. The proposed model offers a significant benchmark for the IES's green performance optimization.

Look-ahead scheduling of energy-water nexus integrated with Power2X conversion technologies under multiple uncertainties

Co-optimizing energy and water resources in a microgrid can increase efficiency and improve economic performance. Energy-water storage (EWS) devices are crucial components of a high-efficient energy-water microgrid (EWMG). The state of charge (SoC) at the end of the first day of operation is one of the most significant variables in EWS devices since it is used as a parameter to indicate the starting SoC for the second day, which influences the operating cost for the second day. Hence, this paper examines the benefits and applicability of a look-ahead optimization strategy for an EWMG integrated with multi-type energy conversion technologies and multi-energy demand response to supply various energy-water demands related to electric/hydrogen vehicles and commercial/residential buildings with the lowest cost for two consecutive days. In addition, a hybrid info-gap/robust optimization technique is applied to cover uncertainties in photovoltaic power and electricity prices as a tri-level optimization framework without generating scenarios and using the probability distribution functions. Duality theory is also used to convert the problem into a single-level MILP so that it can be solved by CPLEX. According to the findings, the implemented energy-water storage systems and look-ahead strategy accounted for, respectively, 4.03% and 0.43% reduction in the total cost.

An efficient and privacy-preserving algorithm for multiple energy hubs scheduling with federated and matching deep reinforcement learning

As a significant paradigm change in reinforcement learning, federated learning (FL) has emerged to address the efficiency bottlenecks and privacy concerns of centralized training. However, the discrepancy of multi-energy demands among energy hubs (EHs) may cause undesirable instability of FL mechanisms. This study proposes a scheduling algorithm for adaptively controlling multiple EHs with combined heat and power, gas boiler, and electrical boiler. We first present the formulations of twin delayed deep deterministic policy gradient (TD3) to schedule the energy conversion under the time-varying price and demands of electricity, heat, and natural gas. The novelty of the proposed algorithm lies in developing a distributed TD3-agents training that consists of FL and matching-based agent-to-agent learning, namely matching learning (ML). Specifically, the FL is first introduced for collaborative training of EH's TD3 agents by sharing their semi-trained scheduling models. The ML innovatively proposed for addressing the training instability of FL is formulated as a matching game in which the poor-trained agents learn the scheduling knowledge from the agent who has analogous demand patterns and better reward. Simulation results display the advantage and feasibility of the proposed algorithm in terms of the speed of training convergence, energy consumption of equipment, and economic benefit.

Application of an intelligent method for hydrogen-based energy hub in multiple energy markets

Use of different energy carriers together, known as an energy hub, has been a hot topic of research. The hub manager manages the energy in the hub and supplies the energy demanded by the consumers. Herein, a multi-carrier energy storage system comprising a thermal energy storage system, ice storage system, and hydrogen storage system is considered for a multi-carrier energy system to exploit their economic benefits. The proposed combined cooling, heating and electricity microgrid not only participates in the electricity, gas and heat market to meet the needs of electricity, heating and cooling, but also can use the new hydrogen power technology used in the hydrogen storage system to increase the efficiency of the whole system participate in the market. In addition, a multi-energy demand response model is used as a new concept of demand response on electrical and thermal loads, which provides more options for multi-energy end users in energy management policies. To cover the uncertainty of the existence of different energy sources in the used model and the price of electricity, a new method has been used. The LHS sampling is used to develop diverse scenarios, and the backward scenario reduction method is adopted to decrease the number of scenarios. To solve the objective function and optimization, the metaheuristic whale algorithm improved by a local search algorithm is utilized. The main goal is to reduce the operation costs of the multi-energy carrier system. The obtained results show that the hydrogen storage utilization and multi-energy demand response can reduce the system operation cost by 6% for the studied experimental system. Furthermore, as the uncertainty budget increased, the total cost of operations increased by 10%. This is because the level of planning conservatism increases with each increase in the uncertainty budget.

Collaborative optimization scheduling of integrated energy system considering user dissatisfaction

Traditional industrial parks generally have the problem of energy waste and low energy utilization. In this paper, we study a multi-energy collaborative optimization problem between integrated energy system (IES) energy scheduling and production control of plant, which considers the change of user dissatisfaction caused by load adjustment after industrial users participate in multi-energy demand response (MEDR). The collaborative optimization problem is described as a quadratic programming (QP) problem, including energy equipment output and production equipment operation load under constraints. The QP problem is solved by Cplex solver. An IES framework is constructed and its components are modeled separately. Then, based on Taguchi loss function and Fanger thermal comfort model, a collaborative optimization model of plant IES considering the user dissatisfaction is proposed. Furthermore, the collaborative optimization model is applied to control the IES and plant production. Simulation results show that the proposed optimization model can reduce the energy cost and user dissatisfaction, and improve the energy efficiency.

Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system

The carbon-capturing process with the aid of CO2 removal technology (CDRT) has been recognised as an alternative and a prominent approach to deep decarbonisation. However, the main hindrance is the enormous energy demand and the economic implication of CDRT if not effectively managed. Hence, a novel deep reinforcement learning agent (DRL), integrated with an automated hyperparameter selection feature, is proposed in this study for the real-time scheduling of a multi-energy system (MES) coupled with CDRT. Post-carbon capture systems (PCCS) and direct-air capture systems (DACS) are considered CDRT. Various possible configurations are evaluated using real-time multi-energy data of a district in Arizona, the United States, and CDRT parameters from manufacturers' catalogues and pilot project documentation. The simulation results validate that an optimised soft-actor critic (SAC) DRL algorithm outperformed the Twin-delayed deep deterministic policy gradient (TD3) algorithm due to its maximum entropy feature. We then trained four (4) SAC DRL agents, equivalent to the number of considered case studies, using optimised hyperparameter values and deployed them in real time for evaluation. The results show that the proposed DRL agent can meet the prosumers' multi-energy demand and schedule the CDRT energy demand economically without specified constraints violation. Also, the proposed DRL agent outperformed rule-based scheduling by 23.65%. However, the configuration with PCCS and solid-sorbent DACS is considered the most suitable configuration with a high CO2 captured-released ratio (CCRR) of 38.54, low CO2 released indicator (CRI) value of 2.53, and a 36.5% reduction in CDR cost due to waste heat utilisation and high absorption capacity of the selected sorbent. However, the adoption of CDRT is not economically viable at the current carbon price. Finally, we showed that CDRT would be attractive at a carbon price of 400-450USD/ton with the provision of tax incentives by the policymakers.

Multi-agent deep reinforcement learning based distributed control architecture for interconnected multi-energy microgrid energy management and optimization

Environmental and climate change concerns are pushing the rapid development of new energy resources (DERs). The Energy Internet (EI), with the power-sharing functionality introduced by energy routers (ERs), offers an appealing alternative for DER systems. However, previous centralized control schemes for EI systems that follow a top-down architecture are unreliable for future power systems. This study proposes a distributed control scheme for bottom-up EI architecture. Second, model-based distributed control methods are not sufficiently flexible to deal with the complex uncertainties associated with multi-energy demands and DERs. A novel model-free/data-driven multiagent deep reinforcement learning (MADRL) method is proposed to learn the optimal operation strategy for the bottom-layer microgrid (MG) cluster. Unlike existing single-agent deep reinforcement learning methods that rely on homogeneous MG settings, the proposed MADRL adopts a form of decentralized execution, in which agents operate independently to meet local customized energy demands while preserving privacy. Third, an attention mechanism is added to the centralized critic, which can effectively accelerate the learning speed. Considering the bottom-layer power exchange request and the predicted electricity price, model predictive control of the upper layer determines the optimal power dispatching between the ERs and main grid. Simulations with other alternatives demonstrate the effectiveness of the proposed control scheme.

Real-time multi-energy demand response for high-renewable buildings

Proactive demand response is a cost-effective approach to enhance energy flexibility of high-renewable urban energy systems. This paper proposes a two-stage real-time multi-energy demand response framework for a high-renewable building microgrid. In the first stage, a rolling horizon-based on-line scheduling model is formulated to economically optimize the building multi-energy converters and storages via a novel transactive pricing mechanism. In the second stage, the schedulable multi-energy flexibility for response devices is defined and evaluated via comprehensive information. After obtaining the stochastic optimal economical scheduling results in the first stage, the second stage performs a rule-based faster-time scale multi-energy allocation to offset the real-time mismatch energy due to supply–demand uncertainties. Case studies over a building microgrid are performed to validate the effective and superior performances of the proposed method on improvements of multi-energy economy and flexibility. Simulations results show that the system operating cost can be reduced by at most 36.9% with a higher operational flexibility.

Can cross-sector information improve multi-energy demand forecasting accuracy?

More and more distributed energy resources are integrated into regional integrated energy systems (RIES), which poses great challenges to energy balance. RIES can coordinate power, gas, heat, and cooling systems jointly to enhance energy efficiency and explore flexibility for distributed renewable energy accommodation. Multi-energy demand forecasts are the basis of the flexible operation of RIES. However, the multi-energy demands are deeply coupled in RIES. Related researches utilize cross-sector information from different sectors to tackle the coupling relationship. Nevertheless, the unknown influence of cross-sector information offered by other sectors varies with the operating pattern, which is difficult to be evaluated. This work proposes an operating pattern recognition-based method for adaptive cross-sector information identification. Firstly, the K-means cluster algorithm is adopted to identify different operating patterns. After that, cross-sector information is selected based on the Pearson coefficient. Furthermore, two models, i.e., the local model and fine-tuned model, are modified with the assistance of selected cross-sector information. The proposed method is evaluated by a RIES with three energy types (electricity, chill water, and steam). The proposed two methods acquire better accuracy than the three benchmark models. Moreover, the Shapley value is applied to verify the contribution of selected cross-sector information. The result shows that all selected cross-sector information plays a significant role in load prediction.

A multi-time scale dispatching optimal model for rural biomass waste energy conversion system-based micro-energy grid considering multi-energy demand response

Aiming at utilizing straw, garbage, domestic sewage and other biomass waste resources in rural areas, this study designed a rural biomass wastes energy conversion system (BWs)-based micro energy grid (BWs-MEG), and the mathematical modeling of BWs-MEG is carried out including multi-energy transforms system (METS) and multi-energy demand response (MEDR). Then, a two-stage optimal framework for rural BWs-MEG multi-time scale dispatching is designed. In the day-ahead stage, a multi-objective dispatching optimal model was constructed with the objectives of minimum operating costs and maximum eco-environment benefits. In the intra-day stage, the robust optimization theory was utilized to characterize the uncertainties of wind power plant (WPP) and photovoltaic power generation (PV) and a rolling dispatching optimal model was constructed with the objective of minimum deviation costs. After that, the above dispatching model was fuzzified and linearized, and then converted into a mixed integer linear programming model. Finally, a micro-energy grid in northern China was selected as an example for case study. The results showed: (1) BWs can utilize rural biomass waste resources for pyrolysis power generation (PG) and gas production to achieve energy utilization and provide power, heating and gas output. In the islanded operation mode and grid-connected operation mode, when MEG is configured with BWs, the deviation costs decrease by 25.6 % and 26.33 %, while the operating costs increases only by 7.60 % and 13.52 %, respectively. The power output of WPP and PV increase by 1.03 % and 2.19 % in the grid-connected operation mode, indicating that BWs is conducive to realizing the multi-dimension supply and demand balance of power, heating and gas loads. (2) Multi-time scale dispatching model can give play to the regulation ability of BWs, METS and MEDR and connect the day-ahead dispatching plan with the intra-day dispatching strategy to formulate the optimal dispatching strategy. When there is deviation in the day-ahead dispatching strategy, METS and MEDR could maintain the supply and demand balance of power load. AHP can change the heating period and BWs could maintain the supply and balance of heating and gas loads. Compared with the day-ahead dispatching plan, the output of PG and power-to-gas device in intra-day dispatching strategy increase by 21.22 % and 9.78 %. (3) Robust stochastic optimization method can characterize the uncertainties of WPP and PV and formulate the dispatching decision schemes with different risk attitudes. With the robust coefficient Г increasing, MEG operating costs and eco-environment benefits increase and deviation adjustment costs decreases, but the robustness of the dispatching scheme is improved. When 0.25≤Г≤0.75, the increase of the uncertainty parameter will have a direct impact on the formulation of dispatching scheme. And the decision maker belongs to risk preference type, who is willing to take certain risks to win excess benefits. Besides, if MEG operates in the grid-connected mode, the uncertainty risks will be weakened and the operation mode shall be reasonably selected according to the demand for dispatching decision. Overall, the proposed optimization model can promote the energy utilization of rural biomass waste resources, which is conducive to the realization of a clean and low-carbon transformation of the overall energy structure.

Day-ahead scheduling of SMR integrated energy system considering heat-electric-cold demand coupling response characteristics

Nuclear heating has the advantages of clean and efficient, especially the all-in-one small modular reactor (SMR) of heating-power generation has the characteristic of decreasing power generation when increasing heating power, that is, the characteristic of thermoelectric reverse coupling, which is helpful to improve the economy of the system and promote the consumption of renewable energy. The multi-energy demand coupling response is an important measure to realize the efficient operation of the integrated energy system (IES). Based on the integrated demand response (IDR), a day-ahead scheduling model of the SMR-IES considering the demand coupling response characteristics is proposed. First of all, based on the advantages of SMR heating and power generation integration, the SMR-IES operating architecture with coupling in both sides of energy supply and demand is proposed. Then, on the basis of energy hub model and power load price elastic response model, the IDR model considering the coupling response characteristics of heat-electric-cold demand is established, which takes into account the mutual influence and restriction of both sides of energy supply and demand, while organically connecting the supply side and demand side. Finally, with the goal of reducing the operating cost of the system, a day-ahead scheduling model is established, which realizes the coordination and optimization of the supply and demand side by adjusting the price of energy. Simulation on the MATLAB platform, and the optimization results of different energy supply scenarios are compared. It is found that based on the SMR-IES scheduling model considering demand coupling response the daily optimal operation cost is reduced by 38.4%, and the renewable energy efficiency is increased by 63.1%. Considering the demand coupling response, the coupling substitution of electricity-heat load can be realized, the electricity-heat load can be adjusted while ensuring that user needs remain unchanged, and the comprehensive energy efficiency of the system can be further improved.

A mixed conditional value-at-risk/information gap decision theory framework for optimal participation of a multi-energy distribution system in multiple energy markets

This study provides a unique risk-based hybrid two-stage operational model for a coordinated power and gas distribution system that enables it to engage optimally in the electricity, gas, and thermal markets in order to serve electricity, gas, and heat demands. The suggested hybrid model enables the integrated energy distribution system (IEDS) operator to apply several risk-averse methods concurrently, based on the nature of the uncertainties and the availability of data for unknown parameters. The conditional value-at-risk-based stochastic programming (CVaR-SP) technique is utilized to cover the uncertainty in wind speed, vehicle behavior in a parking lot, day-ahead energy prices, and multi-energy demands. Furthermore, the infromation gap decision theory (IGDT) model is used to control uncertainty in real-time energy prices without requiring the use of a probability distribution function or the creation of scenarios. To provide a high-flexibility structure to meet a variety of demands, the IEDS is outfitted with some local energy resources, including a gas-fired power-only unit, a gas-fired combined heat and power plant, a wind turbine, a gas boiler, an electric vehicle parking lot (EVPL), multi-energy storages (MESs), and integrated demand response (IDR). According to the numerical data, the IEDS operator can lower the risk level by 2% while raising the operating cost by 1.3%. Furthermore, the simultaneous use of MESs, EVPL in vehicle-to-grid mode, and IDR reduce operating costs by up to 17.5%.

Research on coordinated control of renewable-energy-based Heat-Power station system

The traditional combined heat and power (CHP) units have drawbacks such as a lack of flexibility in control and environmental pollution. Renewable-energy-based heat-power station (REHPS) system utilizes renewable energy to produce electricity and heat with the advantages of cleanness, high efficiency, and flexibility. The previous studies on CHP mainly focus on optimal scheduling, and its operation control is seldom considered. This paper proposes a coordinated control strategy of REHPS to ensure stable and reliable operation. Firstly, the dynamical model of REHPS is established and the operational principle and dynamic characteristics are analyzed. Secondly, a double-layer coordinated control strategy is proposed for smoothing power fluctuation in the lower layer and meeting the demand of thermal load in the upper layer. The dynamic characteristics of the controller are analyzed for the parameter design to match the dynamic response time of the energy storages. Finally, simulations and experimental studies are conducted to demonstrate the effectiveness of the proposed coordinated control in the REHPS system under different scenarios. The results show that the system operates stably and the indoor temperature is maintained at around 25 °C. Compared with the previous study, the maximum voltage overshoot is decreased by 11.2% under wind speed fluctuations and the temperature difference is controlled within about 0.5 °C under thermal load variation under the proposed control. The research on REHPS control provides a new solution to meet the multi-energy demands of users.