Risk-aware low carbon optimization of hydrogen-oriented multi-energy system with linear approximation of bi-product fuel cell

A comprehensive method to assess the feasibility of renewable energy on Algerian dairy farms

Research on life cycle low carbon optimization method of multi-energy complementary distributed energy system: A review

Capacity configuration optimization of multi-energy system integrating wind turbine/photovoltaic/hydrogen/battery

Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials

This paper reviews the work in the areas of optimization and efficiency enhancement of multi-energy systems (MES) for power-to-X conversion. The first study delves into the deployment of Power-to-Hydrogen (PtH2) within district-scale MES, emphasizing the role of PtH2 in achieving zero operational CO2 emissions, especially in systems with high renewable energy generation. The study also highlights the significance of heat pump efficiency, battery capital cost, and lifetime in influencing PtH2 implementation. The second investigation focuses on the integration of energy strategies for the transport and building sectors. It introduces a multi-objective optimization model that considers both sectors, aiming to minimize costs and life-cycle emissions. The findings suggest a potential transition from internal combustion engines to battery electric vehicles and a shift from gas boilers to heat pumps, leading to substantial emission reductions by 2050. Lastly, the third research explores the potential of power-to-gas (P2G) technology in enhancing the integration of renewable energy. By coordinating P2G with CO2-based electrothermal energy storage (ETES), the study demonstrates a significant improvement in the recovery efficiency of surplus wind power. Collectively, these studies underscore the importance of optimizing MES for sustainable and efficient energy conversion.

During recent years, quantum computers have received increasing attention, primarily due to their ability to significantly increase computational performance for specific problems. Computational performance could be improved for mathematical optimization by quantum annealers. This special type of quantum computer can solve quadratic unconstrained binary optimization problems. However, multi-energy systems optimization commonly involves integer and continuous decision variables. Due to their mixed-integer problem structure, quantum annealers cannot be directly used for multi-energy system optimization.To solve multi-energy system optimization problems, we present a hybrid Benders decomposition approach combining optimization on quantum and classical computers. In our approach, the quantum computer solves the master problem, which involves only the integer variables from the original energy system optimization problem. The subproblem includes the continuous variables and is solved by a classical computer. For better performance, we apply improvement techniques to the Benders decomposition. We test the approach on a case study to design a cost-optimal multi-energy system. While we provide a proof of concept that our Benders decomposition approach is applicable for the design of multi-energy systems, the computational time is still higher than for approaches using classical computers only. We therefore estimate the potential improvement of our approach to be expected for larger and fault-tolerant quantum computers.

Multi-objective optimization of multi-energy heating systems based on solar, natural gas, and air- energy

Robust coordinated optimization for multi-energy systems based on multiple thermal inertia numerical simulation and uncertainty analysis

Scalable planning and control of individual devices within multi-energy systems is important to support the energy transition. However, multi-energy systems are complex due to relations between different energy carriers on different levels. This paper extends the Profile Steering algorithm with support for such multi-energy systems using distributed optimization, in which individual components can be added. As concrete application, a method to perform load shedding and curtailment, to balance a local district heating network, is presented. Our evaluation shows that a multi-energy system, consisting of a buffer, a CHP, and a heat pump can be optimized within reasonable time and leads to a reduction in export of 51.4%.

A Time-series-based approach for robust design of multi-energy systems with energy storage

Under the background of carbon neutralization, developing the multi-energy system (MES), which effectively integrates kinds of energy resources, is one of the keys to solving the environmental contradiction. At present, considering heat storage, the research on the optimal scheduling of MES, especially industrial park MES, focuses on the heat storage tank. The potential of the heating supply network (HSN) for heat storage is underestimated and its operational constraints haven’t been fully studied. Therefore, with consideration of quantitative heat storage in HSN, a day-ahead operation scheduling optimization model for MES is proposed. The model proposes a tubular heat storage body (THSB) to describe the heat storage characteristics and charge/discharge constraints of HSN and studies its impact on economic benefits and stability of output power. Equivalent method is used to quantify the heat storage capacity of HSN. Based on the established model, we take a real industrial park to optimize the operation plan of heating conditions in winter. Results indicate that the optimized scenario shows better economic benefits and output stability, which saves 1,303 yuan of operation cost per day.

Renewable energy systems have emerged as a crucial research area due to the escalating demand for sustainable energy solutions. With the advancement of renewable energy, the electric-thermal coupling within multi-energy systems has become more intricate. Bi-directional electric-thermal storage and conversion technologies have emerged as a potential solution to address the challenges associated with efficient energy utilization. This paper focuses on the joint planning and operation optimization of renewable energy systems considering bi-directional electric-thermal storage and conversion. The integrated framework for renewable energy systems incorporating a bi-directional electric-thermal storage and conversion unit is designed, and the joint planning and operation optimization method is proposed. Case studies are conducted based on typical annual energy demand and solar radiation characteristics in Beijing, China. Numerical results show that the proposed method can effectively handle the coupling and bi-directional conversion characteristics of electrical and thermal energy, achieving energy cost savings while fulfilling the energy demands of the system. The proposed system has a capital expenditure of USD 261,251.4 and an operating expenditure of USD 177,007.1, which shows a total cost reduction of 12.28% compared to the lithium-ion battery system, providing better economic performance while further enhancing the flexibility of energy utilization. These research findings contribute to the development of more efficient and sustainable renewable energy systems, providing a valuable reference for future research and practical applications within the energy field.

Design Optimization of Hybrid Renewable Energy Systems for Sustainable Building Development based on Energy-Hub

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.

Operational optimization of a rural multi-energy system supported by a joint biomass-solid-waste-energy conversion system and supply chain