Multi-energy System Research Articles

Rough knowledge enhanced dueling deep Q-network for household integrated demand response optimization

Implementing a household integrated demand response (HIDR) can be an effective solution to save energy and reduce carbon emissions in household multi-energy system (HMES). However, the HMES are subject to uncertainties, and HIDR optimization needs to automatically adapt to the uncertainty and make decisions within 15 min. Therefore, we propose an HIDR optimization method combining rough knowledge and a dueling deep Q-network (DDQN). Firstly, we propose an HIDR optimization framework of rough knowledge and DDQN fusion, in which knowledge is utilized to generate good DDQN samples and serves as action guidance value input to DDQN. Secondly, we formulate the household equipment models and knowledge rules. Subsequently, we design the HIDR optimization algorithm that incorporates rough knowledge into DDQN, focusing on dynamic probability of knowledge sample participation in DDQN learning, knowledge serving as DDQN action adviser, knowledge diversification and dynamic adjustment of DDQN’s random exploration probability. Simulation results show that our method saves energy costs by 5.6% and 0.9% compared to rule-based methods and DDQN, respectively. Additionally, based on an i5-10300H CPU, our method’s convergence time is no more than 13 min even for scheduling interval of 15 min. The average convergence time of our method is 29% of DDQN.

Life-cycle-based multi-objective optimal design and analysis of distributed multi-energy systems for data centers

The widespread deployment of data centers has contributed to a dramatic increase in energy consumption and carbon emissions. To solve this problem, this study proposes a distributed multi-energy system (DMES) driven by solar, wind, geothermal and natural gas and its life-cycle-based multi-objective optimization model considered energy, economy, and environment. Moreover, a novel operation strategy based on load characteristics is presented for energy flow allocation and a multi-objective grasshopper optimization algorithm is improved for model solving. Case studies are conducted on a practical data center in Qinghai, China. Based on the DMES capacity configuration and scheduling results, the cooling, heating and power supply can satisfy the demand of the data center while generating over 30% of the power from renewable sources. In addition, carbon tax is considered in the optimization model, which accounts for 4% of the life-cycle cost. Furthermore, the sensitivity of carbon tax, equipment lifespan and predicted mean vote is analyzed, obtaining that these factors have some influence on the performance of the system. Finally, the uncertainty impact on system planning is studied, concluding that the designed system exhibits high robustness levels when renewable energy forecast error is less than 20% or load forecast error is less than 10%.

Stochastic optimal design of a rural microgrid with hybrid storage system including hydrogen and electric cars using vehicle-to-grid technology

In this paper, the optimal sizing of a rural microgrid is studied by applying two-stage stochastic programming with a scenario-based approach considering a multi-energy system and different electric vehicle technologies with grid-vehicle-grid operations. The system components are a photovoltaic panel, wind turbine, battery, hydrogen-based storage, battery electric vehicle (BEV), and fuel cell electric vehicle (FCEV). A multi-objective optimization problem with minimizing life cycle cost and maximizing the system reliability is defined for the microgrid design. To reflect reliability, loss of power supply probability (LPSP) indice is used. The defined problem is solved with mixed-integer linear programming. Hourly real-world data for a one-year period is used for simulations. The uncertainties from renewable resources, demand, BEV, and FCEV users are considered during system design. Firstly, we analysed the impact of different storage technologies on system design. We found that a hybrid storage system is the most cost-effective option for each LPSP limit and increasing LPSP from 0 to 0.2 led to a cost reduction of up to 20% in all cases. Secondly, different cases are created to assess the impact of BEVs and FCEVs in capacity configuration considering grid-vehicle-grid operations. Simulation results demonstrate that utilizing vehicle-to-grid (V2G) mode in both types of vehicles leads to a cost reduction of approximately 2% when a hybrid storage system is in place. Furthermore, it is observed that BEVs contribute significantly, providing up to 90% of the V2G power. Also, the availability of FCEV with a larger size of fuel tank capacity affects positively the cost of the designed system.

Cooperative scheduling and trading in sustainable water-integrated and networked energy systems incorporating power-to-gas

This paper presents a stochastic and cooperative approach for the operation of a cluster of interconnected multi-energy systems. The proposed model investigates the interaction among energy systems with the upstream grid and neighbor energy systems. In this model, the hydrogen systems (consisting of fuel cell, electrolyzer, and hydrogen storage tank) and water system (water well, desalination unit, and water storage tank) are integrated into the electrical, heating, and cooling systems to provide an efficient and reliable structure for the energy systems. The proposed model studies the performance of energy system agents in both decentralized and cooperative scheduling. In the decentralized framework, each agent is responsible for locally supplying different energy demands with their resources and trading energy with the main grid. In the decentralized framework, peer-to-peer energy trading between neighbor energy systems is impossible. Also, each agent can trade energy with the upstream grid and other neighbor energy systems in the cooperative framework. The simulation result shows that the proposed cooperative model decreases the operation cost of the system by $ 56.9. Also, the water storage system reduces the operation cost by 5.78 % (86.23 $) and 3.28 % (49.61 $) in the cooperative and decentralized modes, respectively.

Multi-objective optimization of multi-energy complementary system based on cascade utilization of heat storage

A multi-energy complementary system driven by solar energy and central grid is proposed to supply electricity and cooling/heating, in which a dual-tank thermal storage system is integrated to achieve cascaded solar heat energy utilization. The system integrates parabolic trough solar collectors, high-temperature and low-temperature thermal storage tanks, and an organic Rankine cycle to form an operational loop. The low-temperature thermal storage tank drives an absorption heat pump independently. Excess photovoltaic power drives the ground source heat pump for heating/cooling. Three indicators are defined and compared to show the renewable energy penetration rate in the complementary system. A multi-objective optimization model considering economic performance and renewable energy rate is constructed to optimize the system configurations. Three sets of objective functions are compared through the optimization Pareto frontiers. Then, the TOPSIS method is employed to select the optimum configuration through setting different weights of optimization objectives. Compared with the reference system, the proposed multi-energy complementary system improves the energy, economic and environmental benefits by 59.21%, 24.27% and 11.31%, respectively. Through a variety of equipment (PV, ORC, AHP, TES), solar energy supplies 86.2% and 72.2% of total electricity and heat loads, respectively.

Distributionally robust optimization for dispatching the integrated operation of rural multi-energy system and waste disposal facilities under uncertainties

With the increasing global attention to environmental protection and sustainable development, governments around the world attach great importance to the low-carbon reform of rural energy and waste management. The interaction between waste-to-energy technology (W2E) and rural multi-energy system has emerged as one of the important issues that must be considered. Based on this, this paper combines two W2E technologies, biomass biogas power generation and garbage incineration power generation, with photovoltaic and energy storage systems. A distributionally robust optimization (DRO) model for the waste-disposal-facilities-involved rural multi-energy system (W-RMES) is proposed. An ambiguity set of the photovoltaic output probability distribution is constructed by the Wasserstein metric. The minimum operation cost of W-RMES is solved under the condition that the prediction error of photovoltaic output obeys the worst probability distribution of the ambiguity set. Finally, through simulation verification, the rational utilization of waste can effectively realize the coordination and optimization between the waste disposal and multi-energy supply of electricity, heat, and biogas. Compared with the conventional rural energy system, W-RMES is more economical and eco-friendlier, which is reflected in the reduction of operation cost by >14.87 %, the removal of flue gas production by >12.89 % and the improvement of waste disposal capacity by >21.77 %. Besides, the model also realizes systemic coordination of robustness and economic efficiency.

Performance assessment of hybrid PEMFC-solar energy integrated hybrid multi-generation system for energy production sport buildings.

Polymer membrane electrolyzers are a useful tool for producing hydrogen, which is a renewable energy source. Unmanned aerial vehicle (UAV) fuel cells can be powered by the hydrogen and oxygen produced by the electrolyzer. The primary losses of polymer membrane electrolyzers must therefore be identified in order to maximize their performance. A renewable-based multi-energy system considers power, cooling, heating, and hydrogen energy as utility systems for integrated sport buildings. In this study, we investigate the effect of radiation intensity, current density, and other performance factors on the rate of hydrogen production in water electrolysis using a polymer membrane electrolyzer in combination with a solar concentrator. The findings showed that a rise in hydrogen generation led to an increase in current density, which increased the electrolyzer's voltage and decreased its energy and exergy efficiencies. The voltage was also increased, and the electrolyzer's efficiency was enhanced by a rise in temperature, a decrease in pressure, and a reduction in the thickness of the nafion membrane. Additionally, with a 145% increase in radiation intensity, hydrogen production increased by 110% while the electrolyzer's energy and exergy efficiencies decreased by 13.8% as a result of the electrolyzer's high input electric current to hydrogen output ratio.

Revolutionizing Green Transport: An Extensive Review of Hybrid Electric Vehicle Charging Stations and Electric Microgrid Integration

Electric vehicles (EVs), recognized as a strategic approach to reducing oil consumption and greenhouse gas emissions, rely on electricity instead of traditional fuels like petrol or diesel for battery charging, positioning them as a significant player in future energy landscapes. The anticipated decline in oil demand aligns with the increasing prevalence of EVs, making attention to charging infrastructure crucial. This paper extensively explores charging infrastructure considerations, emphasizing their significance in both urban and rural contexts, especially in regions with unstable or absent power supplies. Examining off-grid, grid-connected, and hybrid charging modes, the research delves into various EV designs, including those utilizing fuel cells or batteries.
 A thorough understanding of energy-source-based charging techniques and diverse power-level charging stations is presented, catering to readers' interests. With a focus on enhancing the longevity and efficiency of electric vehicles, researchers are investigating innovative charging methods, including microgrid concepts within charging stations. Recognizing electric vehicles as multi-energy systems, the paper underscores the importance of effective power management and control for optimal energy utilization.
 Additionally, the review scrutinizes the impact of electric vehicles on utility grid infrastructure & maintenance, evaluating various power management and control systems. This comprehensive review serves as a valuable resource for electric vehicle operators and research engineers, offering insights into the current state of the field and potential avenues for future exploration.

Research on CCHP Design and Optimal Scheduling Based on Concentrating Solar Power, Compressed Air Energy Storage, and Absorption Refrigeration.

In response to the country's "carbon neutrality, peak carbon dioxide emissions" task, this paper constructs an integrated energy system based on clean energy. The system consists of three subsystems: concentrating solar power (CSP), compressed air energy storage (CAES), and absorption refrigeration (AR). Among them, thermal energy storage equipment in the photothermal power generation system can alleviate the fluctuation of solar energy and provide a stable power supply for the system. The compression heat generated during the compression process of the CAES system can be recovered through heat transfer oil to provide a heat load. The compressed air in the air accumulator (ACC) expands in the air turbine to provide an electric load. The low-temperature exhaust gas discharged from the turbine can provide cool load. First, a multienergy system that includes CSP, CAES, and AR is built. Then, the system takes the lowest economic cost as the objective function and constructs the system day-ahead scheduling model. Finally, for data obtained from scene reduction, the commercial optimization software Gurobi is invoked through YALMIP to solve the model. The results show that the three subsystems achieve multienergy complementarity; system operating costs are reduced by 59.94% and fully absorb wind and solar energies by the system.

Environmental impact and thermodynamic comparative optimization of CO2-based multi-energy systems powered with geothermal energy

The continuous increase in global population and energy demands has enhanced the use of fossil fuels. The evident climate change effect in recent years has brought about the necessity to reduce fossil fuels. Hence, there exists an aggregate problem of meeting energy demands and reducing carbon emissions. Renewable energy sources have been proffered as the probable solution; however, multi-energy systems are effective options/alternatives to solving this problem. In recent literature, geothermal energy has been proposed as a renewable energy source that can continuously meet energy demands. However, there exists a significant gap in literature about the most viable temperature range for geothermal energy applications in multi-energy systems. In this study, two innovative CO2-based systems namely, high-temperature geothermal multi-energy system (HTGMES) and low-temperature geothermal multi-energy system (LTGMES) are designed, modelled, analyzed, and compared using a thermodynamic approach. While the HTGMES is modelled to predominantly use CO2 as a working fluid, a novel modified absorption system is integrated with the LTGMES. The two systems are modelled to produce electricity, cooling/refrigeration effect, space heating, hydrogen, and hot water. The energy, exergy environmental, exergy destruction, and exergoeconomic methodology is used to evaluate the performance of the innovative GMESs. Also, multi-objective optimization of the HTGMES and the LTGMES is carried out in a bid to minimize the total product unit cost and maximize overall exergy efficiency. The environmental impact analysis of the proposed system is presented considering CO2 mitigation. The results from analyses showed that the overall energetic and exergetic efficiencies in steady state are 44. 22 % and 33.5 % for the HTGMES and 45.40% and 32.9 % for the LTGMES. The optimized LCOE, LCOC, and LCOH based on the total unit cost are 0.0573 $/kWh, 0.1833 $/kWh, and 11.59 $/kg for the HTGMES; 0.0323 $/kWh, 0.0032 $/kWh, and 11.9$/kg for the LTGMES. Furthermore, the optimized exergetic performance showed that the LTGMES can achieve as high as 64.54 % and 40.96 % energy and exergy efficiency.

Modelling of multi-energy systems of residential buildings with Calliope and validation of results

Given the current and future importance of energy availability in the residential sector, there is great interest in reducing and optimising the energy consumption of single and multi-family houses. An energy system consisting of solar PV, heat pump, and battery and thermal storage can reduce electricity grid dependence by maximising the use of renewable energy. However, creating a model for such a multi-energy system can be challenging due to the need for detailed input data and high computational cost. This study aimed to develop a simpler model with short computation times and sufficient accuracy by employing a mixed-integer linear programming (MILP) method based on the modelling framework Calliope, allowing the investigation of optimal operation regarding energy consumption and autarky. The results of a model simulation were compared to measurements acquired at a pilot site of a residential building in an alpine region of Switzerland. The results show good agreement between the model and pilot site for PV self-consumption (-5%), grid-export (+3%), and battery usage (-2%), whereas larger discrepancies were observed for the utilisation of the thermal storage (-78%). With the necessary modifications the developed model will be useful in estimating the feasibility and impact of multi-energy systems in residential buildings.

Economic and environmental benefits of decentralized multi-energy systems for energy communities

In the context of growing interest in decentralised multi energy systems, this work aims at quantifying the benefit of optimized energy concepts for energy communities at the neighbourhood scale compared to individually optimized solution. To tackle this question a multi-objective optimization framework was developed and applied to a case study of 6 buildings consisting of 85% of residential dwellings and 15% to retail shop and food stores. Grouped buildings have decreased costs and greenhouse gases emissions (GHGE) respectively by 18% and 12% in the cost optimum compared to individual buildings. In the environmental optimum, costs have decreased by 11%, while GHGE remains in the same range. This decrease is at both optimum driven by electricity prices favourable to large consumers since exchanges on the electrical microgrid for this neighbourhood is very small. Optimal decrease of GHGE is obtained with greater use of HPs and smaller natural gas consumption. This work illustrates the interest of multi-objective approaches to identify optimal energy solutions for groups of buildings.

Veggies and PV: Optimization of Building-Integrated Agriculture in an Energy Hub

Building-integrated Agriculture (BIA) is the concept of utilizing façade surfaces for crop production. The potential is to reduce land-use and to increase the utility of built-up area. If outdoor building surfaces were to be used for farming, it results in a competition for sunlight between crops, solar renewable energy (such as photovoltaics, PV), and daylight access to illuminate indoor spaces. We therefore propose a coupled BIA and multi-energy systems model that can represent various energy sources (such as sunlight) and their conversion and storage (such as façade based crop production, PV, or electric batteries) in order to optimally meet demands for building energy and food. It is formulated as a mixed integer linear program (MILP) optimization model that describes the energy flows as an annual hourly time series. The model conducts a bi-objective minimization of monetary cost and carbon emissions, resulting in a Pareto front of optimal solutions. The model meets building energy demands for cooling, heating and electricity by an optimized energy technology portfolio, as well as nutritional demands for leafy vegetables of all occupants by either BIA or supermarket purchases. We apply our model to a residential case study in Singapore, which serves as an example for a high density city with already numerous community-driven and practiced BIA initiatives ongoing. Our results show that when minimizing cost, utilizing PV is more cost efficient on highly exposed surfaces such as the roof than BIA. However, for more shaded façade surfaces, crop production can be a cost and environmentally efficient addition to building design, as the annual vegetables demand of all occupants can be covered entirely by self-grown produce.

Flexibility assessment of power-hydrogen-power (P2H2P) system in multi-energy districts

Climate change mitigation plans and recent tension over gas supply generate significant incentives for reducing reliance on fossil fuels in the built-environment, with an increase in installing renewables and in electrifying the heating sector. However, the seasonal imbalance of energy supply and demand is expected to increase further. Sector coupling enabled through power-to-hydrogen-to-power (P2H2P) systems that include seasonal storage can partially mitigate this issue. In this work, a hydrogen-based multi-energy system is modelled with a mixed-integer linear programming based Ehub optimization tool. While results show that the P2H2P system is technically and spatially feasible and more flexible, its large hydrogen storage is associated with high costs and embodied carbon emissions. Finally, an outlook for P2H2P systems for integration into renewable-based systems is presented.

Carbon trading-based layered operation optimization of the electric–thermal multi-energy-flow coupling system with photothermal power stations

With the increasing development of low-carbon economy, the coupling degree of electric thermal gas system is deepening day by day. The operation mode of traditional energy supply system using electric heating separation mode and hierarchical dispatching of transmission and distribution network has been difficult to mine the whole network resources and realize the global optimal operation strategy. In order to increase the consumption of new energy and solve the multi-energy current coupling system, this paper puts forward a layered optimization operation strategy for the electric-thermal multi-energy current coupling system with Concentrated solar power (CSP) plant and carbon trading mechanism. The upper layer is the solution layer of multi-energy coupled flow system. In order to solve the matrix values of complex multi-energy coupled flow system, Newton's method, improved Newton’s method and improved second-order cone collaborative solution methods are proposed. The lower layer is the multi-energy flow optimization layer. According to the system solution values obtained from the upper layer, the lower layer optimization aims at economic minimization of the total user cost. The carbon trading mechanism model is introduced to optimize the time sequence output of the electric thermal unit under different scenarios, and the mixed integer linear programming method is adopted. Finally, the validity is verified by a simulation example. The results show that this method can optimize the operation of the system, improve the accuracy and rapidity of the system, and “carbon trading and CSP power station” method can better constrain the carbon emission of the multi-energy flow coupling system, reduce the energy pressure of the device, and improve the economy of the total cost of the user.If this method is adopted, it can be used for reference in the future energy management and system decision of multi-energy flow coupling system.

An adaptive safety layer with hard constraints for safe reinforcement learning in multi-energy management systems

Safe reinforcement learning (RL) with hard constraint guarantees is a promising optimal control direction for multi-energy management systems. It only requires the environment-specific constraint functions itself a priori and not a complete model (i.e., plant, disturbance and noise models, and prediction models for states not included in the plant model — e.g. demand forecasts, weather forecasts, price forecasts). The project-specific upfront and ongoing engineering efforts are therefore still reduced, better representations of the underlying system dynamics can still be learnt, and modelling bias is kept to a minimum (no model-based objective function). However, even the constraint functions alone are not always trivial to accurately provide in advance (e.g., an energy balance constraint requires the detailed determination of all energy inputs and outputs), leading to potentially unsafe behaviour. Furthermore, while computing the closest feasible action results in a high sample efficiency (as in OptLayer), it does not necessarily have a high utility (especially in the initial learning stage of RL agents). In contrast, providing a safe fallback policy a priori can lead to a high initial utility, but was shown to result in a poor sample efficiency and inability to include equality constraints (as in SafeFallback). In this paper, we present two novel advancements: (I) combining the OptLayer and SafeFallback method, named OptLayerPolicy, to increase the initial utility while keeping a high sample efficiency and the possibility to formulate equality constraints. (II) introducing self-improving hard constraints, to increase the accuracy of the constraint functions as more and new data becomes available so that better policies can be learnt. Both advancements keep the constraint formulation decoupled from the RL formulation, so new (presumably better) RL algorithms can act as drop-in replacements. We have shown that, in a simulated multi-energy system case study, the initial utility is increased to 92.4% (OptLayerPolicy) compared to 86.1% (OptLayer) and that the policy after training is increased to 104.9% (GreyOptLayerPolicy) compared to 103.4% (OptLayer) — all relative to a vanilla RL benchmark. Although introducing surrogate functions into the optimisation problem requires special attention, we conclude that the newly presented GreyOptLayerPolicy method is the most advantageous.

Synergistic Strategies in Multi-Energy Systems: Thermodynamic Constraints within Distribution Networks

Synergistic Strategies in Multi-Energy Systems: Thermodynamic Constraints within Distribution Networks

Evaluation of Biogas and Solar Energy Coupling on Phase-Change Energy-Storage Heating Systems: Optimization of Supply and Demand Coordination

Biogas heating plays a crucial role in the transition to clean energy and the mitigation of agricultural pollution. To address the issue of low biogas production during winter, the implementation of a multi-energy complementary system has become essential for ensuring heating stability. To guarantee the economy, stability, and energy-saving operation of the heating system, this study proposes coupling biogas and solar energy with a phase-change energy-storage heating system. The mathematical model of the heating system was developed, taking an office building in Xilin Hot, Inner Mongolia (43.96000° N, 116.03000° E) as a case study. Additionally, the Sparrow Search Algorithm (SSA) was employed to determine equipment selection and optimize the dynamic operation strategy, considering the minimum cost and the balance between the supply and demand of the building load. The operating economy was evaluated using metrics such as payback period, load ratio, and daily rate of return. The results demonstrate that the multi-energy complementary heating system, with a balanced supply and demand, yields significant economic benefits compared to the central heating system, with a payback period of 4.15 years and a daily return rate of 32.97% under the most unfavorable working conditions. Moreover, the development of a daily optimization strategy holds practical engineering significance, and the optimal scheduling of the multi-energy complementary system, with a balance of supply and demand, is realized.

Optimize configuration of multi-energy storage system in a standalone microgrid

Aiming at the integrated energy microgrid, an important part of the energy internet, this paper constructs a multi-energy storage system optimization configuration model of the integrated energy microgrid in an independent mode, and proposes a configuration method that includes the rated power and capacity of the storage system and the heat storage system. The storage system model includes the estimation of battery life during heating and non-heating periods. The model takes economy as the index, and considers the relevant constraints of thermoelectric coupling of thermoelectric units, including thermal and electrical balance, unit climb, energy storage system and self-sufficiency probability, etc., and uses a The bacterial colony chemotaxis (BCC) algorithm model based on unit output and energy storage system power distribution strategy to solve the problem. The operation characteristics of cogeneration units equipped with energy storage system are discussed. The results show that the proposed multi-energy storage system configuration method has significant economic and environmental benefits in both heating and non-heating periods, and promotes the uptake of wind power.

Optimum utilization of hub energy micro-grids with micro-networking strategy of local biogas productions

In this paper, the optimal utilization of local biogas production in multi-energy systems is investigated. The micro-grid policy of local biogas production, to supply the variable demand of natural gas and also to convert it into electrical energy required by an energy hub micro-grid, along with other sources of scattered energy production and national electricity and gas networks, is one of the main goals of this paper. The multi-factor intelligent optimization method is considered to investigate the non-linear and heterogeneous optimization problem in the IEEE standard 33-bus energy hub microgrid. Most of the main indicators of interest are economic savings and minimization of operating costs. The multi-objective optimization method has been chosen as a method to solve the aforementioned non-linear and heterogeneous problem. Simulation has been done in GAMS software and the results of optimal load distribution of electric energy and natural gas supply for selected energy microhub have been explained and compared. The final results show a significant reduction of 46 % (2.8 thousand dollars per day) in operating costs. Significant reduction of energy flow between local network microhubs by 39.4 % is also one of the optimization results studied. Also, the Island performance of the local energy microhub has been simulated with the desired strategy. The cost of local energy microhub energy supply in island mode has decreased by 34 % (1.4 thousand dollars per day) compared to normal mode.© 2022 Published by.