Search

The integration of energy storage and wind power is an effective solution to address the challenge of system stability. Hydrogen energy storage system offers large storage capacities to supply electricity, making it highly promising for large-scale consumption of wind power. However, due to the uncertainty of wind power, the thermal demand of hydrogen energy storage system is uncertain when the system switches operation modes frequently. Therefore, the thermal energy demand of the hydrogen storage system in intermittent mode is considered comprehensively. Initially, the basic structure of the electricity and heat integrated energy system connected to the hydrogen energy storage system is introduced, and a wind-hydrogen hybrid system composed of a wind farm and hydrogen energy storage is described. Furthermore, a robust interval optimization dispatch model for the electricity-heat integrated energy system is proposed, taking into account the uncertainty of the thermal balance demand of the wind-hydrogen hybrid system. Finally, based on the duality theory, the proposed model is transformed into a mixed-integer linear programming problem. By comparing and analyzing the optimization results of various scenarios through numerical examples, the effectiveness of hydrogen energy storage in promoting wind power consumption and increasing the comprehensive energy utilization rate of the system is verified. It is also demonstrated that the system's efficiency can be improved by considering the working temperature of electrolytic and fuel cell, leading to an increase in the grid-connected power of wind farms.

With the implementation of the carbon peaking and carbon neutrality strategy, China's heating system has witnessed various transformative trends, including source-network-load integration and the synergistic utilization of fossil and renewable energy sources. The district heating system, serving as a vital intermediary between heat sources and end-users, plays a crucial role in ensuring a stable energy supply. Particularly, as renewable energy integration increases and end-user requirements become more refined, the demand-supply matching aspect of district heating systems becomes increasingly critical. Therefore, it is essential to quantify and analyze user demand while establishing an adaptive pipe network capable of accommodating fluctuations in end-user load. This paper proposes an optimal topology structure method for district heating systems, taking into account the uncertainty of end-user load. Specifically, a quantitative model of end-user load uncertainty is developed, utilizing the Gram-Charlier A expansion method to capture the factors influencing end-user load uncertainty comprehensively. Furthermore, an optimization model for the pipe network topology is formulated, which considers the uncertainty of end-user load as a constraint and aims to minimize pipeline construction costs. This model optimizes the layout, size, and location of heating sources within the district heating system. The proposed method's practical applicability is validated through a case study of a district heating system in a northern Chinese city that involves 21 end-users. The results indicate that the end-user load uncertainty model outperforms the conventional normal distribution fitting model when dealing with bimodal load distributions. Additionally, the proposed method reduces construction costs by 6.24%, decreases heat loss by 5.30%, and eliminates redundant structures within the district heating system. This research significantly contributes to the planning and design of heating pipeline networks and the optimization and refurbishment of existing pipeline networks within district heating systems.

The ongoing transformation of district heating systems (DHSs) aims to reduce emissions and increase renewable energy sources. The objective of this work is to integrate solar thermal (ST) and seasonal aquifer thermal energy storage (ATES) in various scenarios applied to a large DHS. Mixed-integer linear programming (MILP) is used to develop a comprehensive model that minimizes operating costs, including heat pumps (HPs), combined heat and power (CHP) units, electric heat boilers (EHBs), heat-only boilers (HOBs), short-term thermal energy storage (TES), and ATES. Different ATES scenarios are compared to a reference without seasonal TES (potential of 15.3 GWh of ST). An ATES system with an injection well temperature of about 55 °C has an overall efficiency of 49.8% (58.6% with additional HPs) and increases the integrable amount of ST by 178% (42.5 GWh). For the scenario with an injection well temperature of 20 °C and HPs, the efficiency is 86.6% and ST is increased by 276% (57.5 GWh). The HOB heat supply is reduced by 8.9% up to 36.6%. However, the integration of an ATES is not always economically or environmentally beneficial. There is a high dependency on the configurations, prices, or emissions allocated to electricity procurement. Further research is of interest to investigate the sensitivity of the correlations and to apply a multi-objective MILP optimization.

In U.S. district heating (DH) systems, steam is the most common heat transport medium. Industry demand for new advanced modeling capabilities of complete steam DH systems is increasing; however, the existing models for water/steam thermodynamics are too slow for large system simulations because of computationally expensive algebraic loops that require the solution to nonlinear systems of equations. For practical applications, this work presents a novel split-medium approach that implements numerically efficient liquid water models alongside various water/steam models, breaking costly algebraic loops by decoupling mass and energy balance equations. New component models for steam DH systems are also presented. We implemented the models in the equation based Modelica language and evaluated accuracy and computing speed across multiple scales: from fundamental thermodynamic properties to complete districts featuring 10 to 200 buildings. Compared to district models with the IF97 water/steam model and equipment models from the Modelica Standard Library, the new implementation improves the scaling rate for large districts from cubic to quadratic with negligible compromise to accuracy. For an annual simulation with 180 buildings, this translates to a computing time reduction from 33 to 1–1.5 h. These results are critically important for industry practitioners to simulate steam DH systems at large scales.