Multi-energy System Research Articles

A Coordinated Control Strategy of Multi-Type Flexible Resources and Under-Frequency Load Shedding for Active Power Balance

With the increasing expansion of power systems, there is a growing trend towards active distribution networks for decentralized power generation and energy management. However, the instability of distributed renewable energy introduces complexity to power system operation. The active symmetry and balance of power systems are becoming increasingly important. This paper focuses on the characteristics of distributed resources and under-frequency load shedding, and a coordinated operation and control strategy based on the rapid adjustment of energy storage power is proposed. The characteristics of various controllable resources are analyzed to explore the rapid response capabilities of energy storage. The energy storage types are categorized based on the support time, and the final decision is achieved with power allocation and adjustment control of the energy storage system. Additionally, a comprehensive control strategy for under-frequency load shedding and hierarchical systems is provided for scenarios with insufficient active support. The feasibility of the proposed model and methods is verified via a multi-energy system case.

Techno-economic assessment of hybrid renewable energy system with multi energy storage system using HOMER

Energy storage systems (ESS) address the uncertainty of renewable energy sources (RES) in renewable energy based isolated microgrids. One type of ESS is unable to provide 100 % supply reliability. Moreover, there are either excess energy or reliability issues due to a lack of energy management techniques. Therefore, this paper proposes hybrid RES (HRES) with multiple-ESS (MESS), including battery and hydrogen storage system (HSS). The techno-economic analysis and optimal design of HRES-based microgrids, which include wind turbine (WT), solar photovoltaic (PV), fuel cell (FC), electrolyzer, HSS, and battery energy storage (BES) are performed using HOMER. The design objectives are to minimize the net present cost (NPC) and cost of energy (COE) in the proposed HRES with MESS. The analysis is performed for a multi-energy storage system. The results show that WT/PV/FC/Electrolyzer/HSS/BES/converter is the optimal microgrid with the lowest NPC of $838832, $679605, and COE of 0.232 $/kWh, 0.189 $/kWh at capacity shortages of 0.0 % and 1.0 %, respectively. The excess energy and unmet loads are also the lowest for the system at the same capacity shortage. The sensitivity analysis shows the impact of uncertain techno-economical parameters on NPC and COE.

Impact of tariff structures on energy community and grid operational parameters

This paper presents a mixed-integer linear programming optimization model of a renewable energy community comprised of members with local generators, battery energy storage systems, electric vehicles, and heat pumps and thermal energy storage, thus representing a local multi-energy system. The goal of the paper is to analyze the impact of different tariff structures for incentivizing energy sharing within energy communities on the distribution grid operational parameters. To achieve this, a comprehensive analysis of a hypothetical renewable energy community is conducted using the presented optimization model. Various tariff structures are evaluated, including flat and dynamic tariffs, reflecting the day-ahead market prices for energy and supply, as well as various network tariff designs, such as regulated charge reduction, time-of-use network tariffs and capacity payments. The results indicate that sharing energy within communities reduces energy losses in the grid, peak loads and reverse power flows and results in a smoother voltage profile. It is also found that tariffs that include dynamic electricity and supply costs can further increase these benefits, while including capacity payments as an incentive mechanism yields most favourable results for all analyzed criteria.

Thermodynamic investigation of a Carnot battery based multi-energy system with cascaded latent thermal (heat and cold) energy stores

Carnot batteries store electricity in thermal form, allowing for power balancing and also multi-vector energy management as a unique asset. Cascaded thermal energy storage therefore has a vital role in Carnot battery, particularly multi-energy systems delivering electricity and thermal energy at various temperatures. Here, we propose a Carnot battery multi-energy system with cascaded latent thermal energy stores. The effects of compressor pressure ratio, total stage number, stage area and fluid velocity in tubes, on system-level coefficient of performance and total exergy efficiency are investigated. The results show that both the coefficient of performance and exergy efficiency increase significantly and then level off with the total stage number and stage area increasing, while they only increase slightly before a significant decrease with the increase of fluid velocity in tubes. The selection of compressor pressure ratios relates to the demands of cold, heat and electricity. The multi-energy system achieves a maximum coefficient of performance of 95.0% in combined cooling, heating and power mode. Although the thermodynamic performance yileds comparable with Carnot battery systems integrated with packed-bed or liquid-based sensible heat stores, the Carnot battery multi-energy system can deliver electricity and multi-grade heat and cold simultaneously, thus increasing flexibility in future energy systems.

A cloud-edge cooperative scheduling model and its optimization method for regional multi-energy systems

In the process of multi-energy system optimal scheduling, due to the high data processing requirements of the multi-energy devices and loads and the complexity of the operating states of the multi-energy devices, the scheduling optimization of the system is to some extent more difficult. To address this problem, this paper proposes a regional multi-energy system optimal scheduling model based on the theory of cloud-edge collaboration. First, based on intelligent data sensors, a cloud-edge cooperative scheduling framework of the regional multi-energy system is constructed. Then, the physical model of operating state data of multi-energy system equipment and the allocation mechanism of system scheduling tasks are studied. With the cloud service application layer and the edge computing layer as the upper and lower optimization scheduling layers, the double-layer optimization scheduling model of the regional multi-energy system is established. The objectives of the model are optimal scheduling cost and minimum delay of scheduling data transmission. The multi-objective whale optimization algorithm is used to solve the model. Finally, a simulation model is built for verification. The simulation results show that the scheduling model established in this paper can effectively improve the scheduling data processing capability and improve the economy of regional multi-energy system scheduling.

Collaborative development model and strategies of multi-energy industry clusters: Multi-indicators analysis affecting the development of coastal energy clusters

The paper explores Coastal Energy-Based Industrial Clusters (EBICs) and their role in advancing energy efficiency and sustainability through collaborative innovation. Economic growth theory and energy sustainability have been introduced into industrial clusters to illustrate indicators that have a greater impact on the development of EBICs. This paper proposes an EBICs development model based on the Cobb-Douglas function, in which accounts for various factors that drive the progress of such clusters. The outcomes of the economic model also provide insights into how the interaction of various factors affects the economic growth of EBICs and their eventual dominance in the energy market of coastal regions, dependent on gradually investing in areas such as research and development (R&D). Different development strategies demonstrate that the final development of a cluster has low dependence on the cluster's initial advantages. The study also illustrates how clusters can gradually monopolize the energy market, even with initial disadvantages. Through quantitative analysis, it showcases the transformation process of developing advantageous clusters into sub-clusters. Next, an energy symbiosis framework for coastal is proposed, which places greater emphasis on the multi-energy complementary system and reduces production costs. Finally, this paper also sheds light on shaping energy strategies for public authorities who shape economic policies at various levels of aggregation and in diverse dimensions.

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.

Performance analysis and application of a novel combined cooling, heating and power system integrated with multi-energy storage system

The dynamic fluctuations of user energy demand places the combined cooling, heating and power (CCHP) system in a perpetual state of off-design operation, leading to significant performance deterioration. Integrating an energy storage system with the CCHP system is an effective approach to this issue. However, conventional methods often segregate the storage of cooling load, heating load and power of CCHP system, limiting their conversion and resulting in suboptimal utilization and low efficiency. Therefore, this study proposes a novel CCHP system integrated with multi-energy storage system, allowing flexible release cooling load, heating load and power to meet diverse user load profiles. Mathematical modeling, thermodynamic analysis and environmental assessment are conducted. The case study demonstrates that the proposed system surpasses the conventional CCHP system in exergy efficiency ηex, primary energy rate PER, primary energy saving ratio PESR, carbon dioxide emission reduction ratio CDERR and cost saving ratio CSR of the proposed system increase by 3.1 %, 13.6 %, 9.0 %, 6.2 % and 8.4 %, respectively. The proposed system effectively balances user energy demand and energy supplied by the CCHP system, efficient utilization of diverse energies, significantly enhancing off-design performance, and yielding high efficiency, energy saving, and reduced carbon emissions.

Cost and thermodynamic analysis of wind-hydrogen production via multi-energy systems

With rising temperatures, extreme weather events, and environmental challenges, there is a strong push towards decarbonization and an emphasis on renewable energy, with wind energy emerging as a key player. The concept of multi-energy systems offers an innovative approach to decarbonization, with the potential to produce hydrogen as one of the output streams, creating another avenue for clean energy production. Hydrogen has significant potential for decarbonizing multiple sectors across buildings, transport, and industries. This paper explores the integration of wind energy and hydrogen production, particularly in areas where clean energy solutions are crucial, such as impoverished villages in Africa. It models three systems: distinct configurations of micro-multi-energy systems that generate electricity, space cooling, hot water, and hydrogen using the thermodynamics and cost approach. System 1 combines a wind turbine, a hydrogen-producing electrolyzer, and a heat pump for cooling and hot water. System 2 integrates this with a biomass-fired reheat-regenerative power cycle to balance out the intermittency of wind power. System 3 incorporates hydrogen production, a solid oxide fuel cell for continuous electricity production, an absorption cooling system for refrigeration, and a heat exchanger for hot water production. These systems are modeled with Engineering Equation Solver, and analyzed based on energy and exergy efficiencies, and on economic metrics like levelized cost of electricity (LCOE), cooling (LCOC), refrigeration (LCOR), and hydrogen (LCOH) under steady-state conditions. A sensitivity analysis of various parameters is presented to assess the change in performance. Systems were optimized using a multi-objective method, with maximizing exergy efficiency and minimizing total product unit cost used as objective functions. The results show that System 1 achieves 79.78 % energy efficiency and 53.94 % exergy efficiency. System 2 achieves efficiencies of 55.26 % and 27.05 % respectively, while System 3 attains 78.73 % and 58.51 % respectively. The levelized costs for micro-multi-energy System 1 are LCOE = 0.04993 $/kWh, LCOC = 0.004722 $/kWh, and LCOH = 0.03328 $/kWh. For System 2, these values are 0.03653 $/kWh, 0.003743 $/kWh, and 0.03328 $/kWh. In the case of System 3, they are 0.03736 $/kWh, 0.004726 $/kWh, and 0.03335 $/kWh, and LCOR = 0.03309 $/kWh. The results show that the systems modeled here have competitive performance with existing multi-energy systems, powered by other renewables. Integrating these systems will further the sustainable and net zero energy system transition, especially in rural communities.

Energy systems capacity planning under high renewable penetration considering concentrating solar power

With the rapid development of renewable generation and the promotion of “Clean Heating”, the uncertainty of renewables as well as rising energy demand has posed significant problems to the energy system’s economical and flexible operation. As a renewable energy generation technology, concentrating solar power (CSP) with thermal energy storage (TES) offers a promising approach by providing operational flexibility and thermal energy supply to the energy system. We propose a long-term high-resolution planning model considering the operational characteristics of CSP and improved flexibility constraints of energy systems under high renewable penetration to meet the electricity and heat demand simultaneously in this paper. By applying the proposed model to a typical “Clean Heating” area in China, it demonstrates that the optimal deployment by integrating CSP can reduce the peak-valley depth of coal units, minimize the renewable curtailment and carbon emissions by 37.64 % and 19.05 % respectively with more than 5.98 % cost reduction at 85 % renewable penetration compared with the base scenario, which provides an economic and stable zero-carbon pathway for multi-energy systems.

Enhancing efficiency and reliability of multi-energy systems: A hybrid heuristic algorithm for interconnected energy hubs

In response to the escalating demand for sustainable energy solutions, the integration of diverse energy sources has gained prominence. Energy hubs, facilitating the amalgamation of multiple sources, enhance system efficiency and flexibility. Yet, coordinating the operation of such multi-energy systems remains challenging due to complexity and uncertainties, especially with renewable sources. This paper introduces a Cournot model-based approach for optimizing three interconnected energy hubs. The objective is to minimize total operating costs while ensuring reliability and efficiency. Leveraging the Cournot model, commonly employed in energy studies for competition and pricing, we capture the strategic behavior of energy hubs. The proposed solution employs a modified butterfly flame heuristic algorithm to navigate the non-linear and non-convex optimization problem, efficiently seeking global optimal solutions. Numerical experiments on a three-hub test case validate the method, demonstrating a 3.5 % reduction in total operation costs compared to the base case and over 2.5 % cost reduction for each hub. The proposed algorithm outperforms traditional methods in terms of convergence speed and solution quality, offering a promising avenue for optimizing the operation and management of multi-energy systems. In summary, this paper introduces a novel approach grounded in the Cournot model and a hybrid heuristic algorithm for optimizing three interconnected energy hubs, effectively addressing uncertainties associated with renewable sources and ensuring reliable and efficient system operation.

Control of multi-energy drying system: Optimal weighted combination prediction of moisture content and fuzzy compensation of wind speed

In order to monitor the moisture content accurately and improve the drying uniformity of the same batch of materials, an online moisture monitoring approach based on optimal weighted combination model (OWCM) was developed, and a fuzzy control strategy of wind speed compensation was established for multi-field coexistence. The Human Machine Interface (HMI) and remote monitoring modules are designed, which can automatically obtain, view and store environmental parameters on site and mobile devices. The system was loaded into a kelp drying equipment, and moisture content prediction and drying uniformity verification tests were conducted respectively. The results showed that Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the OWCM are 0.022 and 0.019, the prediction accuracy was better than that of single model and average weighted combined model (AWCM). The drying time difference between the front and back of the same batch of kelp was shortened from 1 ∼ 1.5 h to 0 ∼ 0.5 h. This study provides theoretical and technical support for the synergistic utilization of multiple energies and the research and development of aquatic products processing equipment.

Comprehensive evaluation of the working mode of multi-energy complementary heating systems in rural areas based on the entropy-TOPSIS model

BackgroundThe establishment of a multi-energy complementary heating system (MECHS) using abundant renewable energy is currently an effective way to solve the rapid growth of heating demands in rural areas. However, coupling schemes for different energy sources have become a critical barrier to the MECHS development. ObjectivesThe entropy-TOPSIS method was used to conduct a comprehensive evaluation of MECHS, which provided a basis for optimizing system operation strategy. MethodsWe built a test platform for MECHS based on solar-biomass-electricity energy in Tumxuk, Northwest China. Frist, six working modes were established according to the system operation control strategy. Second, we constructed the evaluation indexes of MECHS considering energy, environment and economy and determined their weights using entropy weight method. Third, the TOPSIS model was used to perform a comprehensive evaluation of the different working modes to identify optimal energy supply schemes. ResultsOur case study showed that the MECHS operated stably and met the indoor temperature demands of rural houses in cold regions. To maximize the heating efficiency of the system, it is necessary to reduce the types of energy appropriately. Next, based on entropy-TOPSIS model, the recommended order of energy supply mode for heating systems were solar-biomass energy (0.6202), solar-biomass-electric energy (0.6152), solar-electric energy (0.5895), biomass energy (0.4075), biomass-electric energy (0.3680), and electrical energy (0.3603). We found that the optimal energy supply mode was solar-biomass. ConclusionOur results indicated that the entropy-TOPSIS model could provide practical recommendations for the design and operation strategies of MECHS in cold rural areas.

A Low-Carbon Optimal Operation Method for an Industrial Park Multi-Energy Coupling System Utilizing By-Product Hydrogen

To enhance the utilization efficiency of by-product hydrogen and decrease the power supply expenses of industrial parks, local utilization of by-product hydrogen plays a crucial role. However, the methods of utilizing by-product hydrogen in industrial parks are relatively limited. In response to this issue, an optimization method for a multi-energy system with by-product hydrogen considering the production process of chlor-alkali plants was proposed in this paper. Firstly, on the source side, models were established for hydrogen production using the ion exchange membrane electrolyzer and for the energy consumption during the caustic soda solution evaporation process. Secondly, on the load side, this paper explored the potential for local utilization of by-product hydrogen, including its participation in the production of downstream chemical products, combustion when mixed with natural gas, and utilization in hydrogen fuel cells. Next, this paper considered the influence of correlations among various loads within the factory and wind power generation, proposing a method for generating scenarios that takes into account the spatiotemporal correlation of source-load variables. Then, aiming to minimize the system operation cost and carbon trading cost, an operation strategy for a multi-energy system in a low-carbon industrial park, considering local utilization of by-product hydrogen, was proposed. Finally, the effectiveness of the scenario generation method proposed in this paper, considering spatiotemporal correlation, and the economic and environmental benefits of the proposed operation model utilizing the by-product hydrogen are verified through arithmetic simulation, based on the operation data of a chlor-alkali chemical park.

Iterative convex relaxation of unbalanced power distribution system integrated multi-energy systems

This study describes a multi-period, multi-energy operation of a three-phase unbalanced Electric Distribution System (EDS) that coordinates with a District Heating System (DHS) and a Natural Gas (NG) distribution system. The electrical subsystem problem is formulated as a bi-level programming problem, with levels 1 and 2 solving the subsystem’s linearized and relaxed nonlinear versions, respectively. Second-Order Cone Programming (SOCP) and polyhedral relaxations are availed to circumvent the non-convexities involved in the subsystems. The SOCP relaxation for an unbalanced power distribution network is inexact; also, the subsystems applying polyhedral relaxation may not generate a meaningful solution as the relaxations are not tight. Therefore, a solution recovery algorithm is developed for each subsystem to recover a feasible solution from their relaxed counterparts. The successive bound tightening algorithm employing a solution recovery procedure is proposed for each subsystem, improving solution quality and strengthening the relaxations with the desired computational efficiency. The proposed solution strategy to optimize the Multi-Energy System (MES) operation cost is verified on the three-phase IEEE-13 and IEEE-123 bus systems, each coordinating with a 30-node DHS and a 6-bus NG network. The results analyses demonstrate that the proposed solution strategy efficiently achieves an optimal solution, reducing maximum relaxation error below 0.1% for each subsystem. The proposed strategy delivered a 14.58% reduction in real power losses and a 12.73% decrease in phase voltage unbalance rate for the EDS in MES. Furthermore, a 1.96% decrease in operational cost demonstrates the techno-economic benefits of the proposed strategy.

A case study of multi-energy complementary systems for the building based on Modelica simulations

The utilization of renewable energy sources and the development of energy storage technologies have enhanced the interconversion of various energy sources to slow down global warming. The building sector, as the main bearer of energy consumption, is essential for its energy saving and emission reduction. Integrating natural gas-based combined cooling, heating, and power systems with renewable energy generation facilities is an effective solution. For that, different scenarios are designed to provide energy for a specific office building and compared with the current scenario of depending only on the municipal grid. Equipment models for photovoltaic, battery, gas engine, absorption chiller-heater, and electric heat pump are created. The results show that the multi-energy complementary system has better economic and environmental characteristics, with a primary energy ratio of over 80 % and emission reduction of over 17 %; the advantages of configuring the unit capacity to meet the cooling and heating loads for 80 % of the year are obvious, and in this scenario abandoning battery storage has higher economics for users, with a static payback period from 7.3 years to 4.4 years, but the primary energy ratio decreased by 3 % and carbon emissions increased by 7.2 tCO2. It is recommended to consider the principle of gas-electricity complementarity, relying on the combined cooling, heating, and power system to meet part of the loads, with the remaining loads being supplemented by the municipal grid. The installation of batteries should be considered comprehensively based on the price of the batteries and the requirement for energy-saving and emission-reduction.

Optimal Operation Scheduling of Multi-energy Complementary Systems Including Offshore Wind Power

Background: With the rapid development of offshore wind power in China, offshore wind power is accounting for an increasing proportion of the whole installed power generation in China, which put forward higher requirements for operation scheduling of multi-energy complementary systems including offshore wind power. However, existing studies have paid little attention to the complementary output scheduling of multi-energy systems with a high proportion of offshore wind power, this phenomenon is not conducive to the realization of reasonable operation scheduling. Objective: To realize the reasonable operation scheduling of multi-energy systems with a high proportion of offshore wind power. Methods: Firstly, the output characteristics of offshore wind power were analyzed and summarized, and modeling was conducted from the perspectives of time distribution, spatial correlation, volatility and randomness, which would serve as the theoretical basis for subsequent researches. Secondly, the complementary rule among offshore wind power and the multi-energy system was analyzed, and the complementary characteristic index was put forward. Then the safety operation was taken as the constraint, and the two-stage operation model of the integrated energy system was established, which considers the auxiliary service costs. In the first stage, the initial generation scheduling was developed according to the day-ahead forecast values of the offshore wind power; in the second stage, the more accurate intra-day operation scheduling was further achieved by considering the intra-day forecast values of offshore wind power and the day-ahead scheduling results. Costs of rotating reserve capacity could be reduced by correcting the deviation of generating capacity, then the revenue-maximizing scheduling strategies were obtained and the output of the pumped-storage power plants was optimized. Results: The effectiveness of the model proposed in this paper was verified based on the simulation examples. Compared with the traditional scheduling strategy, the scheduling strategy considering auxiliary service cost proposed in this paper can promote the consumption of offshore wind power, reduce the wind curtailment proportion and improve the electricity sales revenues in the end. Conclusion: The research results in this paper provide a reference for the reasonable consumption of multi-energy complementary systems with a high proportion of offshore wind power and improving electricity sales revenues

False Data Injection Attack and Corresponding Countermeasure in Multienergy Systems

Worldwide ambitions to combat climate change have expedited the penetration of renewables and decarbonization. Multienergy systems (MES), where different energy systems are optimally coordinated, have been recognized as a key element in future low-carbon energy operations. However, MES operations require intense exchanges of information and control signals, which inevitably intensify the risk of cyberattacks. Existing cybersecurity research mainly focuses on a single-energy system, with only a few pioneering cybersecurity analyses for MES focusing on uncoordinated cyberattacks. A lack of detailed discussion and analysis on the optimally coordinated cyberattack targeting MES prevents operators from accurately evaluating the potential damages of cyberattack in MES operations. Therefore, this paper first proposes an optimally coordinated false data injection attack (OC-FDIA) against MES, where attacks from different energy systems are coordinated to disturb the MES operation. Then, we show that the OC-FDIA can cause synergetic effects leading to much more severe damage than single-system FDIAs and uncoordinated FDIAs. Further, an effective countermeasure is developed to mitigate the OC-FDIA based on deep learning (DL). Eventually, the proposed OC-FDIA and its countermeasures are demonstrated through integrated electricity and gas test systems.

Research on optimization of multi-energy coupling source-network-load-storage based on micro energy network

Based on the architecture of the micro energy network system, a “source network load storage” multi-energy coupling hub system for regional energy internet with electricity gas interconnection is proposed. The lowest operating is set as the optimization goal for the operation of the regional energy internet. A dynamic economic scheduling model for regional energy interconnection based on the sky and an optimization scheduling model for regional energy internet are established. A mixed integer linear programming program based on Matlab is written and solved using CPLex. The results show the operation of the “source grid load storage” regional energy internet with multiple energy coupling hubs as the core is lower than that of traditional micro-grids When adding an energy storage system, the daily operating of the micro-grid system is 16.7% less than that of Control Scenario 1 and 14.3% less than that of Control Scenario 2 The proposal of a regional energy internet “source network load storage” multi-energy coupling hub system based on the micro energy network system architecture provides a feasible basis for the planning and construction research of the regional energy internet.

Predictive Control and Benefit Sharing in Multi-Energy Systems

Predictive Control and Benefit Sharing in Multi-Energy Systems