Multi-energy Sources Research Articles

A Distributed Multi-Timescale Dispatch Strategy for a City-Integrated Energy System with Carbon Capture Power Plants

In city-integrated energy systems containing electric–thermal multi-energy sources, the uncertainty of renewable energy sources and the fluctuation of loads challenge the safe, efficient, economic and stable operation of the integrated energy systems. This paper introduces a novel approach for the operation of a carbon capture plant/CHP with PV accommodation within a city-integrated energy system. The proposed strategy aims to maximize the utilization of photovoltaic (PV) power generation and carbon capture equipment, addressing issues related to small-scale CHP climbing constraints and short-term output regulation. Additionally, this paper presents a multi-timescale optimal scheduling strategy, which effectively addresses deviations caused by PV fluctuations and load changes. This was achieved through a detailed analysis of the CHP climbing constraints, carbon capture equipment operation and real-time characteristics of PV power generation. This paper introduces a fully distributed neural dynamics-based optimization algorithm designed to address multi-timescale optimization challenges. Utilizing rolling cycles, this algorithm computes both day-ahead and real-time scheduling outcomes for urban integrated energy systems. Theoretical analyses and numerical simulations were conducted to validate the precision and efficacy of the proposed model and algorithm. These analyses quantitatively evaluate the scheduling performance of PV power generation and carbon capture CHP systems across various timescales.

Low-carbon environment-friendly economic optimal scheduling of multi-energy microgrid with integrated demand response considering waste heat utilization

- The integration of multi-energy sources in microgrids offers a promising approach to address the challenges of energy efficiency and bolster environmental stewardship. This paper presents a novel optimization scheduling model for multi-energy microgrids (MEMG) with carbon capture and storage (CCS) technology in various renewable energy scenarios. The model effectively coordinates integrated demand response (IDR) and the flexible operation of waste heat utilization, enhancing energy utilization efficiency while ensuring user satisfaction. Furthermore, introducing flexible operation combined with ladder-type carbon trading minimizes operating costs and carbon emissions. The improved particle swarm optimization (PSO) algorithm is employed to solve the eco-economic economic scheduling model. Simulation analysis shows that integrating CCS into MEMG can reduce carbon emissions by 74.4%, while lowering the overall cost by 37.9%. These findings not only improve MEMG's low-carbon and economic efficiency but also pave the way for environmentally responsible energy management practices.

Optimal allocation method of shared energy storage in multienergy coupled microgrid with carbon emission constraint considering dual uncertainty of source and load

Abstract In order to realize the stable operation of the multienergy coupled microgrid under the low-carbon constraint, a carbon emission constrained multienergy coupled microgrid shared energy storage optimization configuration method considering the dual uncertainty of source and load is proposed. This method constructs a source load double uncertainty and carbon emission model, and it analyzes the output characteristics and load demand characteristics of multiple coupled energy sources and the carbon emission results under these characteristics. According to the analysis results, the multienergy coupling microgrid shared energy storage optimization configuration model is constructed, and after the optimization configuration objective function and constraint conditions are determined, the bat algorithm is used to solve it, and the microgrid shared energy storage optimization configuration scheme that meets the objective function and constraint conditions is obtained. The test results show that after the method is applied, the utilization rate of multi-energy sources reaches 90.6%, and the expected carbon emissions are lower than 2.04 × 104kg. The microgrid fluctuation suppression rate reached 92.7%, and the net load fluctuation range was significantly reduced. Thus, it ensures reliable power supply of the microgrid and has good applicability.

Multi-Agent Deep Reinforced Co-Dispatch of Energy and Hydrogen Storage in Low-Carbon Building Clusters

Low-carbon communities integrate various energy producers and consumers and have been forming an important architecture for future multi-energy system to tackle challenges in improving energy utilization efficiency, alleviating environmental pollution, and achieving reliability of energy supply systems. This paper proposes a multi-building network model containing hydrogen energy storage systems, and verifies the applicability of the multi-agent deep deterministic policy gradient (MADDPG) algorithm in solving the multi-energy coordinated dispatch of a multi-building network by comparing various energy dispatch methods. First, the model of hydrogen storage system based on liquid organic hydrogen carrier (LOHC) is completed by unifying the process of electrolysis, storage and release of energy from hydrogen storage system, and an energy dispatch method is further constructed by utilizing the hydrogen storage system, heat storage system, and renewable energy generation equipment in the building for coordinated energy supply to the load. Secondly, the observation space and action space of grid-interactive building multi-energy dispatch in the network are respectively established by considering the ability of MADDPG algorithm to learn deterministic policies for continuous actions. Finally, the advantage of grid-interactive buildings containing hydrogen storage systems to reduce operating costs is validated in a numerical simulation based on three dispatch scenarios for six buildings. Four deep reinforcement learning (DRL) algorithms are used to compare the operating costs with model predictive control (MPC) algorithms in a network model containing six grid-interactive buildings, and validate the advantages of MADDPG in optimizing the cost of co-dispatching multi-energy sources in networked grid-interactive buildings.

Complementary scheduling rules for hybrid pumped storage hydropower-photovoltaic power system reconstructing from conventional cascade hydropower stations

The reconstruction of conventional cascade hydropower plants (CHP) into hybrid pumped storage hydropower plants (HPSH) by adding a pumping station has the potential to increase the hydropower's flexibility and promote the consumption of renewable energy into the power grid. However, the complex hydraulic and electric connections between cascade hydropower stations and multi-energy sources pose challenges to safe and economic operation. This study explores the complementary scheduling for hybrid pumped storage hydropower-photovoltaic (HPSH-PV) system and evaluates the operation benefit and risk. First, the complementary scheduling rules that consider the demand for long-distance and across-regions power transmission are proposed to guide the peak-shaving operation of the system. Then a case study is performed with a hydro-PV complementary project in China, and the benefit and risks are evaluated quantitatively while considering the uncertainty of PV power prediction. The results demonstrate that after a pumping station is added (1) the power generation profit can be increased, especially in the dry seasons and the upstream reservoir; (2) the regulation capacity and power-supply reliability of the upstream reservoir is increased, while that of the downstream reservoir is slightly reduced; (3) the reservoir water level fluctuation and operation risk increase, but it can be lowered as the inflow increases.

Optimal Energy Distribution of Multi-Energy Sources in Fuel-Cell Electric Bus Using Long Short-Term Memory

Optimal Energy Distribution of Multi-Energy Sources in Fuel-Cell Electric Bus Using Long Short-Term Memory

Cogeneration transition for energy system decarbonization: From basic to flexible and complementary multi-energy sources

Energy system transition towards carbon neutrality often involves ambitious substitution of renewable sources for fossil fuels and investment in energy storage to provide regulative flexibility. Yet, radical development of renewable sources may lead to serious asset stranding of existing fossil fuel power, thereby increasing the carbon reduction costs. In this study, new carbon neutralization opportunities are explored in the context of integrated heat and power systems in high latitudes through complementarity of cogeneration retrofit and energy converters. Cogeneration retrofit is an economical option to address the carbon abatement straits as a forerunner and can postpone the construction of flexible energy converters and energy storage for about 10 years. This alleviates potential asset stranding of fossil fuel power, reducing the overall cost by about $ 1.53 billion in Liaoning Province, China. As carbon neutralization progresses, the cogeneration acts as an indispensable backup resource to ease the weather-extreme-induced power shortage.

Economic Dispatch of AC/DC Power System Considering Thermal Dynamics

Advance in renewable penetration has promoted the interaction between the AC and DC power system and multi-energy sources. However, the disparate nature of different energy utilization technologies presents strong challenges for the economic dispatch of such a complex system. This paper proposes a comprehensive characterization of the AC/DC power system considering multi-energy and renewable integration. Detailed models are elaborated for the AC/DC power grid, the district heating system (DHS), coupling units, and renewables to describe their inner interactions accurately. On this basis, an economic dispatch method is developed to minimize the operation cost and renewable’s abandonment. Simulations indicate that the interaction between the AC/DC power systems and multi-energy sources can enhance voltage levels and improve operational economy.

New multi-energy sources coupling a low-temperature sustainable central heating system with a multifunctional relay energy station

Due to short cost-effective heat transportation distance, the existing geothermal heating technologies cannot be used to develop the deep hydrothermal type geothermal fields situated far away from urban area. To solve the problem, a new multi-energy source coupling a low-temperature sustainable central heating system with a multifunctional relay energy station is put forward. As for the proposed central heating system, a compression heat pump integrated with a heat exchanger in the heating substation and a gas-fired water/lithium bromide single-effect absorption heat pump in the multifunctional relay energy station are used to lower return temperature of the primary network step by step. The proposed central heating system is analyzed by using thermodynamics and economics, and matching relationships between design temperature of return water and main line length of the primary network are discussed. The studied results indicate that, as for the proposed central heating system, the cost-effective main line length of the primary network can approach 33.8 km, and the optimal design return temperature of the primary network is 23 ℃. Besides, annual coefficient of performance and annual exergy efficiency of the proposed central heating system are about 3.01 and 42.7%, respectively.

Optimizing Energy Management and Case Study of Multi-Energy Coupled Supply for Green Ships

The ship industry is currently facing numerous challenges, including rising fuel prices, limited fuel resources, and increasingly strict regulations related to energy efficiency and pollutant emissions. In this context, the adoption of green-ship wind–photovoltaic–electricity–fuel multi-energy supply systems has emerged as an efficient and clean technology that harnesses multiple energy sources. These systems have the potential to increase the utilization of renewable energy in ship operations while optimizing management practices in order to enhance overall energy efficiency. To address these challenges, this article presents a comprehensive energy supply system for ships that integrates multi-energy sources for cold–heat–electricity supply. The primary components of this system include fuel cells, photovoltaic equipment, wind turbines, electric heating pumps, electric refrigerators, thermal refrigerators, batteries, and heat storage tanks. By ensuring the safety of the system, our approach aims to minimize daily operating costs and optimize the performance of the multi-energy flow system by running scheduling models. To achieve this, our proposed system utilizes dynamic planning techniques combined with ship navigation conditions to establish an optimized management model. This model facilitates the coordinated distribution of green ship electricity, thermal energy, and cooling loads. The results of our study demonstrate that optimized management models significantly reduce economic costs and improve the stability of energy storage equipment. Specifically, through an analysis of the economic benefits of power storage and heat storage tanks, we highlight the potential for reducing fuel consumption by 6.0%, 1.5%, 1.4%, and 2.9% through the use of electric–thermal hybrid energy storage conditions.

Low-carbon optimal operation of electricity–heat–gas​ systems based on bi-directional tiered-pricing carbon trading

In the context of carbon peak and carbon neutrality target of China, multi-energy systems have become a key way to meet the needs of a low-carbon economy in the energy sector, and can achieve optimization and integration of multi-energy sources. For the purpose of maximize the low-carbon and economic of multi-energy systems, this paper proposes an optimization operation strategy of electricity–heat–gas integrated energy systems (EHGIES) based on bi-directional tiered-pricing carbon trading. The paper first establishes a multi-energy system model with CHP system and P2G conversion, introduces a bi-directional tiered-pricing carbon trading mechanism. Subsequently, an optimization model with objective function of the lowest carbon trading cost and energy purchase cost is established. The nonlinear optimization model is converted to a linear model by the CPLEX solver. Finally, this paper analyzes the trading mechanism, the interval length of tiered-pricing and carbon trading price on carbon emissions and carbon trading cost of EHGIES. The results show that different carbon trading price ranges and price growth rates have different impacts on carbon emissions and carbon trading costs. At the same time, the bi-directional tiered-pricing carbon trading mechanism established can reduce the overall carbon emissions effectively and is conducive to the low-carbon operation, and the low-carbon and economic nature of system operation can effectively be taken into account.

Energy Hub Planning and Operation Optimization by Applying Demand Side Management Using GAMS

Energy hub planning and optimization considering various constraints is an emerging domain of research due to the scarcity of conventional energy resources. Strategic integration of multi-energy sources is a dominant region of research as it provides better efficiency and cost savings. Various uncertainties due to the changing electric, heating, and cooling load demand are increasing the complexity of the system. The paper investigates the effectiveness of Demand Side Management (DSM) programs by analyzing the operation of domestic Energy Hub. The paper proposes two stages for optimal energy hub planning and operation optimization and GAMS mathematical modeling is used to deduce the results. The paper provides the analysis of a two-stage optimization model using the approach of modeling uncertainties and constraints. The process of energy hub designing has been carried out by the help of PSO algorithm by considering continuous capacities of various components of energy hub. The research regarding optimal energy hub operation is modelled in GAMS and the approach of mixed-integer non-linear programming (MINLP) has been used to evaluate the results. The framework provides the energy hub model and also provided the verification of model effectiveness and efficiency.

Blockchain Enabled Decentralized Local Electricity Markets With Flexibility From Heating Sources

Electric power systems are transitioning towards a decentralized paradigm with the engagement of active prosumers (both producers and consumers) through using distributed multi-energy sources. This paper proposes a novel Blockchain based peer-to-peer trading architecture which integrates negotiation-based auction and pricing mechanisms in local electricity markets, through automating, standardizing, and self-enforcing trading procedures using smart contracts. The negotiation of the volume and price of the peer-to-peer electricity trading among prosumers is modeled as a cooperative game, and the interaction between a retailer and its ensemble of prosumers is modeled as a Stackelberg game. The flexibility provision from residential heating systems is incorporated into the energy scheduling of prosumers. Case studies demonstrate that the proposed architecture in local electricity markets helps improve local energy balance. Flexibility from the residential heating systems enables prosumers to be more responsive to the variation of retail electricity prices. The proposed model reduces 41.24% of average daily electricity costs for individual prosumers or consumers compared to the case without the peer-to-peer electricity trading.

Double deep Q-network guided energy management strategy of a novel electric-hydraulic hybrid electric vehicle

The configuration and development of energy management strategies (EMSs) are generating considerable interest regarding vehicles due to the rapid blossoming momentum of electric vehicles. Battery state of charge is one of the main characterization parameters for evaluating EMSs, so the practical advancement of the range is critical to the evolution of electric vehicles. A novel electric-hydraulic hybrid electric vehicle (EHHEV) is investigated in this paper, which has the characteristics of various working modes and multi-energy sources. According to the hybrid system's energy flow, a rule-based control strategy is established, and the superiority of EHHEV in energy management is verified by steady-state simulation. Further, this paper combines Q-learning with deep neural networks to construct a double deep Q-network (DDQN)-guided EMS to solve traditional control strategy and reinforcement learning issues. After appropriate hyperparameters setting and batch training, the EMS can make EHHEV realize the optimal switching among working modes. Experimental results showcase that the EMS can significantly enhance vehicle economy. This is the first of its kind to apply the DDQN to developing EMS for EHHEV.

Multi-energy dispatching for uncertainty EV demand: A simulation approach

Uncertainty and randomness in demand and supply bring significant challenges to the stable operation of the grid and the scheduling planning of multi-energy sources. To solve these challenges, we propose and analyze a multi-energy dispatching model which minimizes the total cost and enhances the efficiency of supplying power. Specifically, we design matching algorithms that simulate an appropriately scaled sequence of stochastic EV demand. We also analyze four different energy dispatching scenarios proving that the scheduling model and the multi-energy synergistic microgrid structure can bring higher efficiency and lower costs. Our main contribution is using a simulation approach to take EVs into account for demand-side uncertainty, which significantly improves the efficiency of grid dispatch.

An integrated fuzzy multi-energy transaction evaluation approach for energy internet markets considering judgement credibility and variable rough precision

Energy internet is an effective way to fulfill carbon neutrality, since it has the advantages of high-efficiency energy utilization. As an important part in energy internet market, multi-energy transaction reflects complementarity of multi-energy sources and provides guidance for future development. However, there still lacks an effective index system for multi-energy transaction evaluation and a reliable method to flexibly handle the subjectivity and vagueness during the evaluation process. In this paper, an effective index system and its extraction method and a comprehensive evaluation approach are proposed to reliably assess the multi-energy transaction performance of the energy internet market. Firstly, an interval type-2 fuzzy decision-making trial and evaluation laboratory (IT2F DEMATEL) method and λ-adversarial interpretive structural modelling (λAISM) approach are combined to extract a 14-indicator index system, the proposed method not only investigates the interrelationship between the indicators but also calculates the influence degree between them. Secondly, to control the vagueness and uncertainty involved in expert judgement, a novel non-deterministic method, combining fuzzy comprehensive evaluation and credibility-based analytic hierarchy process (AHP) with variable rough precision theory, is developed to evaluate the multi-energy transaction performance. Finally, two real-world demonstration projects in northern and southern China are investigated for case studies. The evaluation score of the multi-energy transaction in DZZ town has improved by 25.77 from 2018 to 2021, reaching a “good” performance of 78.09; while the score of XB town has improved by 19.34, reaching an “excellent” performance of 84.32 in 2021. Comparative analysis shows that the extracted index system contains the minimum required information, and the proposed evaluation method can effectively handle the linguistic imprecision and cognition vagueness caused by expert judgements.

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

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

A Dual-Adaptive Equivalent Consumption Minimization Strategy for 4WD Plug-In Hybrid Electric Vehicles.

Energy management strategies are vitally important to give full play to energy-saving four-wheel-drive plug-in hybrid electric vehicles (4WD PHEV). This paper proposes a novel dual-adaptive equivalent consumption minimization strategy (DA-ECMS) for the complex multi-energy system in the 4WD PHEV. In this strategy, management of the multi-energy system is optimized by introducing the categories of future driving conditions to adjust the equivalent factors and improving the adaptability and economy of driving conditions. Firstly, a self-organizing neural network (SOM) and grey wolf optimizer (GWO) are adopted to classify the driving condition categories and optimize the multi-dimensional equivalent factors offline. Secondly, SOM is adopted to identify driving condition categories and the multi-dimensional equivalent factors are matched. Finally, the DA-ECMS completes the multi-energy optimization management of the front axle multi-energy sources and the electric driving system and releases the energy-saving potential of the 4WD PHEV. Simulation results show that, compared with the rule-based strategy, the economy in the DA-ECMS is improved by 13.31%.

Online learning predictive power coordinated control strategy for off-road hybrid electric vehicles considering the dynamic response of engine generator set

For off-road hybrid electric vehicles (HEV), due to the variability of road environments and the dynamic and deferred response characteristic of the engine generator set (EGS) for off-road HEVs, it is difficult to coordinate the power output of multi-energy sources to meet the demand power of the vehicle. Therefore, designing an efficient power control strategy for off-road HEVs remains a major challenge. Motivated by this issue, an online learning predictive power coordinated control strategy for off-road HEVs is proposed in this study. Firstly, the online sequential extreme learning machine is used for short-term power prediction for the first time. With the online learning capability, the precision of power prediction under irregular road conditions is significantly improved. Secondly, to determine the optimal control behavior of power distribution between two energy sources, a novel predictive adaptive equivalent consumption minimization strategy is designed. The equivalent factor is rolling optimized in the prediction horizon to maintain battery state of charge and ensure fuel economy. Thirdly, considering the actual response process of EGS, a one-step-ahead coordinated control is presented to guarantee adequate electric power output. Finally, the performance of the proposed strategy is verified by simulation and hardware-in-loop test. The results show that fuel consumption using the proposed strategy is reduced by 6.72% and 8.63% over benchmark method under the two test driving cycles, respectively. Meanwhile, the setting time of EGS power is decreased by 61.12% and 64.63% to ensure the dynamic performance of vehicle.

Multipoint Layout Planning Method for Multienergy Sources Based on Complex Adaptive System Theory

Summary. With the continuous expansion of grid-connected power generation from renewable energy sources, such as wind and light, the forms of power sources are more diversified. The coupling and uncertainty of multiple energy sources will cause significant changes in the characteristics of the power system. Problems like the complexity of the power system lack a scientific and complete explanation. This paper comprehensively considers the regional weather conditions, the uncertainty of the output of wind and solar energy sources, and the time and space complementary characteristics of multiple power sources. Based on the theory of complex adaptive systems, a systematic and holistic optimization planning method is studied. Combining the idea of complementarity because of differences, a multipoint layout planning and design method for multienergy power systems is proposed to give full play to the synergistic effects of natural resources, achieve optimal resource allocation, and provide a theoretical basis for improving the consumption of wind and renewable energy. Taking an actual provincial power system in China as an example, through the comparison of production simulation calculations and multiscenario analysis and planning methods, the proposed method can significantly increase the consumption of wind and solar renewable energy.