Combined Heat And Power Units Research Articles

Environmental impacts analysis of Moroccan olive cake combustion in Stirling motor cogeneration system: Case study

Morocco is the world's 5th largest olive oil producer with 160,000 tons in 2020/2021 and the production of 1 L of olive oil generated approximately 5 kilos of waste olive cake, this type of waste is dangerous for the environment and especially for groundwater. To address this issue and take into account the thermal properties of olive waste, our project's goal is to produce power and heat by using solid olive waste in a Stirling cogeneration system. Therefore, the goal of this article is to examine by using CFD tools the environmental impacts of the Moroccan solid olive waste burning in a CHP (combined heat and power) unit. The idea is to integrate a smart renewable energy system based on olive cake and the Stirling motor to produce electricity in order to employ this electrical energy for water pumping in rural areas.The impact of the excessive air ratio on temperature profile and exhaust has been investigated and discussed. In addition, the pollutant species in air has been presented, and compared with guideline values given by some international organization for health and environment. Results show that the highest excess air ratio gives high temperatures with a homogeneous distribution around the Stirling motor hot heat exchanger, and minimum emissions of CO, CO2, NO, NO2 and NH3 that do not exceed the values set by WHO, OSHA, and NIOSH.

Performance evaluation and operation optimization of a combined heat and power plant integrated with molten salt heat storage system

Combined heat and power is an effective way to enhance the energy efficiency of thermal power plants. The solar and wind power are intermittent, and dispatchable power sources are critical to balance the supply and demand sides. To facilitate the high penetration of renewable energy, combined heat and power (CHP) plants should provide more and more peak shaving services for the power grid. The thermal energy can be stored in and released from the molten salt heat storage system (MSHSS). The integration of MSHSS can enlarge the adjustable range of the power load of CHP plants, so it is a potential way to enhance the operational flexibility of CHP plants supplying industrial steam and power. In this study, the MSHSS integrated within CHP plants are proposed, which store a part of sensible heat energy carried by extraction steam before supplying to industrial users. The heat storage capacity dominates the flexibility enhancement, and indicators to evaluate energy performances of the proposed system are defined. Moreover, models for the operation optimization of CHP plants integrated with the MSHSS are developed, and a case study of a 350 MW CHP unit is conducted. When the reheat steam is extracted to supply to industrial steam, the maximum and minimum adjustable power loads can be increased and decreased by 17.13 and 12.01 MW, respectively with the integration of the MSHSS. Moreover, during the peak, flat, valley time of reference day, the heat supply coal consumption rates are 73.94, 74.19 and 81.03 kg t−1,respectively. With the operation scheduling of CHP plant integrated with MSHSS, the MSHSS with reheat steam as heat source show the lowest coal consumption rate as 1553.79 ton/day.

Multi-objective scheduling and optimization for smart energy systems with energy hubs and microgrids

This paper introduces a novel model for optimizing microgrid systems by integrating multi-purpose renewable energy (MEM) and cutting-edge technologies, including electric vehicles (EVs). The proposed MEM demand response programs encompass various energy sources such as wind energy, multi-carrier energy storage technologies, boilers, combined heat and cooling units, EVs, P2G (power-to-gas), and demand response capabilities. The primary objective is to minimize the total operational cost of the microgrid system. A distinctive aspect of the proposed method lies in considering the prices of all energy carriers as unknown variables. Market prices are integrated into the modeling process, incorporating scenarios with reasonable probabilities and taking into account demand-side management programs. Moreover, the model allows for customizable programming of different parts of the multi-energy microgrids, with a focus on maintaining convexity principles for the operating area of each CHP (combined heat and power) unit. To tackle the unique complexity of this optimization problem, a developed blue whale optimization algorithm is proposed. This algorithm builds upon the main whale optimization algorithm (WOA), a promising population-based optimization approach. However, the effectiveness of WOA heavily relies on the careful setting of exploration and exploitation parameters, which may lead to being trapped in local optima. To address this challenge, the paper introduces a self-adaptation modification based on wavelet theory to enhance the WOA's performance. The proposed model and optimization method are thoroughly evaluated through simulation studies for different scenarios, showcasing their efficacy in achieving cost-efficient and sustainable microgrid operation. The combination of MEM and emerging technologies like EVs, along with the improved optimization algorithm, marks a significant contribution to advancing the planning and operation of microgrid systems towards low-cost, reliable, and environmentally-friendly energy solutions.

Optimal bidding strategy for integrated CHP system by modified heuristic algorithm considering CO2 emission in energy and reserve markets

Purpose This study aims to solve optimal pricing and power bidding strategy problem for integrated combined heat and power (CHP) system by using a modified heuristic optimization algorithm. Design/methodology/approach In electricity markets, generation companies compete according to their bidding parameters; therefore, optimal pricing and bidding strategy are solved. Recently, CHP units are significantly operated by generation companies to meet power and heat, simultaneously. Findings For validation, it is shown that profit is improved by 0.04%–48.02% for single and 0.02%–31.30% for double-sided auctions. As heat price curve is extracted, the simulation results show that when CHP system is integrated with other units results in profit increase and emission decrease by 3.04%–3.18% and 2.23%–4.13%, respectively. Also, CHP units significantly affect bidding parameters. Originality/value The novelties are pricing and bidding strategy of integrated CHP system is solved; local heat selling is considered in pricing and bidding strategy problem and heat price curve is extracted; the effects of CHP utilization on bidding parameters are investigated; a modified heuristic and deterministic optimization algorithm is presented.

Numerical Study on Peak Shaving Performance of Combined Heat and Power Unit Assisted by Heating Storage in Long-Distance Pipelines Scheduled by Particle Swarm Optimization Method

Thermal energy storage in long-distance heating supply pipelines can improve the peak shaving and frequency regulation capabilities of combined heat and power (CHP) units participating in the power grid. In this study, a one-dimensional numerical model was established to predict the thermal lag in long-distance pipelines at different scale levels. The dynamic response of the temperature at the end of the heating pipeline was considered. For the one-way pipe lengths of 10 km, 15 km and 20 km, the response times of the temperature at the distal end were 2.33 h, 2.94 h and 3.54 h, respectively. The longer the flow period, the further the warming-up time is delayed. An optimization scheduling approach was also created to illustrate the peak shaving capabilities of a CHP unit combined with a long-distance pipeline thermal energy storage component. It was demonstrated that the maximum heating load of the unit increased up to 503.08 MW, and the heating load could be expanded in the range of 17.88 MW to 203.76 MW at the minimum electric load of the unit of 104.08 MW. Finally, the particle swarm optimization method was adopted to guide the operating strategy through a whole day to meet both the electric power and heating power requirements. For the optimized case, the comprehensive energy utilization efficiency and the exergy efficiency increase to 64.4% and 56.73%. The thermal energy storage applications based on long-distance pipelines were simulated quantitively and proved to be effective in promoting the operational flexibility of the CHP unit.

A multi-objective dispatching model for a novel virtual power plant considering combined heat and power units, carbon recycling utilization, and flexible load response

To optimize the energy supply potential and complementary advantages of distributed energy, this paper focuses on the dispatching optimization of cogeneration virtual power plant considering uncertainty. First of all, wind power, photovoltaic, combined heat and power (CHP) units, electric boilers, and controllable loads are integrated into a CHP virtual power plant. Then, carbon capture and electric-to-gas devices are introduced to realize carbon recycling. Furthermore, quantify the risk of real-time dispatching of virtual power plants through uncertainty scenario generation and conditional value at risk (CVaR) theory, and the multi-objective stochastic dispatching optimization model of virtual power plants is built with the aim at minimizing the operation cost, carbon emissions, and operation risk as the objectives, and the CRITIC weighting method is adopted to solve it. Finally, the calculation results show that: 1) the electric boiler can use wind and photovoltaic power to supply heat for the system, reduce the dependence of the virtual power plant (VPP) on the CHP unit, and make the electric output of the unit more flexible. 2) The risk quantification method proposed can fully measure the risk situation in real-time dispatching, arrange the wind and photovoltaic power generation plan and backup plan more reasonably, and enable the VPP to get more benefits while avoiding the risks in real-time dispatching.

Integrated energy system for low-carbon economic operation optimization: Pareto compromise programming and master-slave game

An integrated energy system (IES) can effectively solve the energy crisis, realize multi-energy complementarity, and promote fine-grained energy development. Aiming at the low-carbon economy problem of IESs with combined heat and power (CHP), carbon capture systems (CCSs) and power-to-gas (P2G), the joint operation mode of CHP-CCS-P2G is proposed, and the output characteristics and carbon emissions of CHP units under this mode are analysed. On the supply side, a multi-objective optimization method based on compromise programming is proposed to solve the contradiction between economic and environmental objectives in the IES, and the energy operator balances economic and environmental performance to obtain the optimal configuration scheme and operation strategy. On the load side, a master-slave game energy trading strategy considering integrative demand response (IDR) is adopted to balance the interests of the energy operator and users centrally managed by the load aggregator to ensure the fairness of transactions between the two parties. The results show that the overall revenue of the energy operator decreases by 7.99 % after considering IDR. However, the carbon emissions decreased by 933.82 kg, the carbon trading revenue increased by 45.18 %, and the energy purchase cost of users decreased by 9.34 %.

Low-Carbon Economic Dispatch of Virtual Power Plant Considering Hydrogen Energy Storage and Tiered Carbon Trading in Multiple Scenarios

Reducing carbon emissions and increasing the integration of new energy sources are key steps towards achieving sustainable development. Virtual power plants (VPPs) play a significant role in enhancing grid security and promoting the transition to clean, low-carbon energy. The core equipment of the VPP, the CHP unit, utilizes a thermal engine or power station to generate electricity and useful heat simultaneously. However, the intermittent and volatile nature of renewable energy sources, as well as the “heat-driven power generation” mode of combined heat and power (CHP) units, presents contradictions that severely affect their peak-shifting capability and lead to high carbon emissions. To address these issues, a novel VPP is established by integrating traditional power plants with carbon capture and hydrogen energy storage. This approach utilizes a “hydrogen energy storage–electric boiler” decoupling method to address the operational mode of CHP, strengthens the coupling relationship between electric and thermal hydrogen loads, and considers a tiered carbon-trading mechanism. With the net profit of the VPP as the optimization objective, the model balances economic and environmental considerations and establishes a low-carbon economic dispatch model for the VPP. A genetic algorithm is employed for solving, and three different dispatch strategies are set for simulation in three distinct seasonal scenarios. The comprehensive comparative analysis of the dispatch results reveals a reduction in carbon emissions and an increase in net profit to varying degrees across all three seasons. Overall, the proposed dispatch strategy demonstrates the ability to enhance the new energy-integration capacity and total revenue of a VPP while simultaneously achieving the goal of reducing carbon emissions.

Efficient Microgrid Management with Meerkat Optimization for Energy Storage, Renewables, Hydrogen Storage, Demand Response, and EV Charging

Within microgrids (MGs), the integration of renewable energy resources (RERs), plug-in hybrid electric vehicles (PHEVs), combined heat and power (CHP) systems, demand response (DR) initiatives, and energy storage solutions poses intricate scheduling challenges. Coordinating these diverse components is pivotal for optimizing MG performance. This study presents an innovative stochastic framework to streamline energy management in MGs, covering proton exchange membrane fuel cell–CHP (PEMFC-CHP) units, RERs, PHEVs, and various storage methods. To tackle uncertainties in PHEV and RER models, we employ the robust Monte Carlo Simulation (MCS) technique. Challenges related to hydrogen storage strategies in PEMFC-CHP units are addressed through a customized mixed-integer nonlinear programming (MINLP) approach. The integration of intelligent charging protocols governing PHEV charging dynamics is emphasized. Our primary goal centers on maximizing market profits, serving as the foundation for our optimization endeavors. At the heart of our approach is the Meerkat Optimization Algorithm (MOA), unraveling optimal MG operation amidst the intermittent nature of uncertain parameters. To amplify its exploratory capabilities and expedite global optima discovery, we enhance the MOA algorithm. The revised summary commences by outlining the overall goal and core algorithm, followed by a detailed explanation of optimization points for each MG component. Rigorous validation is executed using a conventional test system across diverse planning horizons. A comprehensive comparative analysis spanning varied scenarios establishes our proposed method as a benchmark against existing alternatives.

Solving the Combined Heat and Power Economic Dispatch Problem in Different Scale Systems Using the Imperialist Competitive Harris Hawks Optimization Algorithm.

The aim of electrical load dispatch (ELD) is to achieve the optimal planning of different power plants to supply the required power at the minimum operation cost. Using the combined heat and power (CHP) units in modern power systems, increases energy efficiency and, produce less environmental pollution than conventional units, by producing electricity and heat, simultaneously. Consequently, the ELD problem in the presence of CHP units becomes a very non-linear and non-convex complex problem called the combined heat and power economic dispatch (CHPED), which supplies both electric and thermal loads at the minimum operational cost. In this work, at first, a brief review of optimization algorithms, in different categories of classical, or conventional, stochastic search-based, and hybrid optimization techniques for solving the CHPED problem is presented. Then the CHPED problem in large-scale power systems is investigated by applying the imperialist competitive Harris hawks optimization (ICHHO), as the combination of imperialist competitive algorithm (ICA), and Harris hawks optimizer (HHO), for the first time, to overcome the shortcomings of using the ICA and HHO in the exploitation, and exploration phases, respectively, to solve this complex optimization problem. The effectiveness of the combined algorithm on four standard case studies, including 24 units as a medium-scale, 48, 84, units as the large-scale, and 96-unit as a very large-scale heat and power system, is detailed. The obtained results are compared to those of different algorithms to demonstrate the performance of the ICHHO algorithm in terms of better solution quality and lower fuel cost. The simulation studies verify that the proposed algorithm decreases the minimum operation costs by at least 0.1870%, 0.342%, 0.05224%, and 0.07875% compared to the best results in the literature.

Optimal Operation of a Village Energy System Considering Renewable Resources and Battery Energy Storage

In this paper, a village energy system is coupled with the wide-area energy network based on the energy hub concept. The village is modeled as an isolated energy hub while it could interact with the energy sub-networks including the electrical, and natural gas through the market. The energy demand of village is supplied with renewable resources such as photovoltaic (PV) system and other generation such as combined heat and power (CHP) technologies. The CHP unit is modeled by the part-load performance. In addition, the battery energy storage is presented beside the PV system to damp the intermittent generation of PV and to be utilized in the high prices of the market. The optimal operation problem is solved to maximize the profit from the energy hub operator point of view. The optimization problem is solved with the teaching-learning based optimization (TLBO) method. The results show that the proper interaction of the village energy system with the upstream sub-networks is so affordable for the system operator to increase the profit, in a way that the maximum benefit of village energy system is reached to 3.6456$.

Optimal Configuration of Power-to-Heat Equipment Considering Peak-Shaving Ancillary Service Market

The serious problem of wind power curtailment in northern China has created a pressing need to enhance the peak-shaving ability of the power system. As the main source of power supply in northern China, combined heat and power (CHP) units have significant potential for peak-shaving. Currently, the Chinese government encourages CHP plants to increase their peak-shaving capacity by installing power-to-heat (P2H) equipment. In addition, the government has implemented auxiliary service market policies to encourage CHP plants to provide peak-shaving services. In order to maximize economic benefits for CHP plants, this paper proposes an optimal configuration method of P2H equipment with the static payback time (SPT) as the objective function. Cost and income models of installing the P2H equipment are constructed by taking into account the auxiliary service market policies. The peak-shaving income model of the CHP plant is derived emphatically as a key part of the proposed method. Finally, the district heating region in Jilin province is used as a case study example. The results show that adding the P2H equipment is significantly effective in improving the peak-shaving ability of CHP units, and investing in heat pumps is more cost-effective than electric boilers. The proposed method can be applied to other northern regions relying on CHP units for central heating, providing a valuable solution to the problem of wind power curtailment in these regions.

The dynamic economic emission dispatch of the combined heat and power system integrated with a wind farm and a photovoltaic plant

Two renewable energies are included in the combined heat and power (CHP) system to optimize its energy configuration, and they are wind power generation and photovoltaic power generation, respectively. Furthermore, a global nondominated sorting genetic algorithm II (GNSGA-II) is proposed to confront the combined heat and power dynamic economic emission dispatch (CHPDEED) with renewable energies. GNSGA-II produces offspring individuals by a crossover with negative exponential distribution, and it is able to carry out global search in the decision space. GNSGA-II also assigns an adaptive weight to original crowding distance to give consideration to both crowding degree and evenness of each individual in the objective space, which is beneficial for improving the evenness of the Pareto set. In addition, a constraint handling approach is proposed to satisfy all constraints, such as power generation limits, heat generation limits, capacity limits of the CHP units, power balances, heat balances, ramp rate limits and spinning reserve requirements. Seven multi-objective evolutionary algorithms (MOEAs) are used to solve the four CHPDEED scenarios with or without renewable energies, and GNSGA-II outperforms the other six MOEAs. It does not only obtain larger hypervolumes and coverage rates, but also obtain relatively small spacings. For the four compromise solutions of GNSGA-II, the generation costs of Scenario 2, Scenario 3 and Scenario 4 are, respectively, 0.51%, 0.34% and 4.1% higher than that of Scenario 1. In the meantime, the pollutant emissions of Scenario 2, Scenario 3 and Scenario 4 are, respectively, 54.78%, 19.05% and 71.45% lower than that of Scenario 1.

A constraint equivalent model of heat network for combined heat and power systems cooperation considering uncertainties

The cooperation of combined heat and power (CHP) systems can improve efficiency in energy utilization by fully utilizing the complementarity between heat and power. However, heat and power systems are operated by different companies where data sharing is restricted for privacy reasons, thus posing significant obstacles to collaboration. This paper proposes a coordinated operation method for CHP systems based on the constraint equivalent model. To preserve the operational constraints of heat networks and protect data privacy, a boundary point search method is firstly developed, where the operational constraints of heat networks are mapped to the feasible region of CHP units. Then, a multi-period cooperation model considering uncertainties of both networks is established based on the proposed constraint equivalent model and the rolling horizon strategy. Moreover, the impact of heat storage tanks and uncertainties of heat loads on the boundaries of the feasible region for heat networks is further analyzed. Simulation results demonstrate the effectiveness of the proposed constraint equivalent model and the cooperation method.

An optimal load curtailment method for operational risk assessment of integrated electricity, natural gas and heating systems

The rapid development of gas-fired generators and combined heat and power (CHP) units promotes the wide application of integrated electricity, natural gas and heating systems (IEGHSs). IEGHSs bring many opportunities to energy supply systems, but also produce huge challenges for ensuring secure and reliable energy supply. Therefore, it is indispensable to investigate the risk assessment for IEGHS operations, especially considering the complexity of system operations. First, a steady-state system model of IEGHSs is developed and linearized taking into account system operation characteristics, including generator operation constraints and the flexibility of CHP units. To reduce computation complexity, an optimal load curtailment method using a mixed integer linear programming algorithm is proposed, which considers both energy types and consumer importance of IEGHSs. Moreover, a contingency set based on the N-1 guideline is constructed taking into account the failures of IEGHSs with high probability. Additionally, the risk indices of IEGHSs are developed in two hierarchies: contingency level and system level. Furthermore, the operational risk of IEGHSs under the constructed contingency set is quantitatively evaluated using the proposed load curtailment method and the developed risk indices. Finally, case studies demonstrate the necessity of operational risk assessment and the effectiveness of the proposed method.

Resilience-oriented placement of multi-carrier microgrids in power systems with switchable transmission lines

Extreme events such as floods, hurricanes, earthquakes, and wildfires pose significant threats to the uninterrupted supply of electricity to consumers, as they can cause the failure of numerous power system components. The resilience of a power system is defined by its ability to withstand extreme events and continue meeting demand. While the placement of multi-carrier microgrids (MGs) in power systems can impact resilience, the literature lacks research on resilience-oriented placement. This paper aims to address this gap by proposing a resilience-oriented placement strategy for multi-carrier MGs, utilizing switchable transmission lines to enhance the resilience of power systems. The study focuses on a heat-power-hydrogen MG that obtains gas from a gas network, exchanges power with the main power system, and incorporates combined heat and power (CHP), electrolyzer, and thermal storage. The developed model employs a stochastic mixed-integer linear programming (MILP) approach, ensuring the attainment of the global optimum. Resilience is evaluated using the metric of expected load not supplied (ELNS). The results demonstrate that when the bus connected to the MG is isolated, the MG generates electricity through its CHP unit to meet local demand, reducing the total demand shed in the power system and improving system resilience. Specifically, the MG reduces ELNS from 4955.48 MWh to 2356.64 MWh, indicating a remarkable 52% improvement in ELNS. Furthermore, the study shows that transmission line switching further decreases ELNS from 2356.64 MWh to 848.68 MWh. Several experiments are conducted to analyze the sensitivity of power system resilience to the number of MGs, the MG-power system exchange limit, and the limit on gas import from the gas network.

Artificial Intelligence Methods for Analysis and Optimization of CHP Cogeneration Units Based on Landfill Biogas as a Progress in Improving Energy Efficiency and Limiting Climate Change

Combined heat and power generation is the simultaneous conversion of primary energy (in the form of fuel) in a technical system into useful thermal and mechanical energy (as the basis for the generation of electricity). This method of energy conversion offers a high degree of efficiency (i.e., very efficient conversion of fuel to useful energy). In the context of energy system transformation, combined heat and power (CHP) is a fundamental pillar and link between the volatile electricity market and the heat market, which enables better planning. This article presents an advanced model for the production of fuel mixtures based on landfill biogas in the context of energy use in CHP units. The search for optimal technological solutions in energy management requires specialized domain-specific knowledge which, using advanced algorithmic models, has the potential to become an essential element in real-time intelligent ICT systems. Numerical modeling makes it possible to build systems based on the knowledge of complex systems, processes, and equipment in a relatively short time. The focus was on analyzing the applicability of algorithmic models based on artificial intelligence implemented in the supervisory control systems (SCADA-type systems including Virtual SCADA) of technological processes in waste management systems. The novelty of the presented solution is the application of predictive diagnostic tools based on multithreaded polymorphic models, supporting making decisions that control complex technological processes and objects and solving the problem of optimal control for intelligent dynamic objects with a logical representation of knowledge about the process, the control object, and the control, for which the learning process consists of successive validation and updating of knowledge and using the results of this updating to determine control decisions.

Dynamic modeling and flexible control of combined heat and power units integrated with thermal energy storage system

The high percentage of renewable energy connected to the grid requires high operational flexibility of combined heat and power (CHP) units. As two products of the CHP units, electricity and heat are coupled, making it challenging to achieve coordinated control. The thermal energy storage (TES) system can smooth out the fluctuations in heat load demand by storing or releasing heat, thus aiding in the decoupling and coordinated control of electricity and heat. In this paper, an optimized control strategy of CHP units integrated with the TES system is proposed to improve the units’ operating flexibility. First, according to the operating mechanism, the dynamic models of the CHP unit and the TES system are established. On this basis, considering the heat source steam active response based on the linear quadratic regulator and the heat-supply support of the TES system, the boiler-turbine-heating-storage coordinated control system of a CHP unit is designed. The simulation experiment is conducted in a 300 MW CHP unit. The results show that compared with the conventional control strategy, the MAE, MAPE, MSE, and RMSE indicators of the electrical power output are decreased by 45.88%, 45.28%, 71.95%, and 47.04%, respectively.

Dynamic Optimization of Variable Load Process for Combined Heat and Power Unit Based on Sequential Quadratic Programming and Interior Point Method Alternating Solution Method

Aiming at the problem that the modeling and solving method of combined heat and power (CHP) unit variable load control process is challenging to meet the demand for efficient analysis of complex systems, this paper proposes a method based on sequential quadratic programming and interior point method (SQP-IPM) alternating solution for dynamic optimization of the CHP unit variable load process. Firstly, by constructing the CHP unit mechanism model, multi-variable coordination control constraints, and output variable process constraints, the dynamic optimization proposition of the CHP unit variable load control process is formed. Then, the large-scale nonlinear programming (NLP) problem formed by using the orthogonal configuration method to discrete the state and control variables is optimally solved using the IPM-SQP alternating solution method. Further, from the perspective of balancing the accuracy of the solution and computational efficiency, the flexible convergence depth control (CDC) strategy is introduced into the alternative solution method to improve the real-time performance of the algorithm. Finally, the variable load control process of 300MW extraction CHP unit is simulated to verify that the proposed method reduces the calculation time for 12 consecutive variable load scenarios by about 70%, effectively improving the real-time performance of scenario applications.

Potential to balance load variability, induced by renewable power, using rock cavern thermal energy storage, heat pumps, and combined heat and power in Sweden

Large shares of variable renewable electricity (VRE) generation increase the demand for flexible power balancing capacities for handling power surpluses and deficits. Within district heating (DH) production systems, electricity can be produced in combined heat and power (CHP) plants but also consumed in heat pumps, and thus contribute with balancing capacity. However, this power balancing potential in DH production units is limited by heat load variations. To improve the potential, large-scale thermal energy storage (TES) can be used to increase heat-load flexibility. This study investigates the power balancing capacity of 85 existing Swedish DH systems, with hypothetical access to rock cavern oil depots assumed to have been converted into TES units. In the study, the Swedish power load is assumed to be covered by 60 % wind and 10 % solar power. The results show that Swedish DH systems, on a national scale, could reduce power deficits and surpluses by approximately 9 % respectively. There will be competition between heat pumps and CHP units for DH load supply while providing power balancing services. The use of heat pumps could also, on national level, yield a reduced fuel use in DH production by about 10 % when compared to conventional DH system operation. The study highlights the impact the temporal distribution and annual shares of VRE surpluses and deficits have on the fuel use.