Heat Demand Response Research Articles (Page 1)

Heat demand response of end users based on energy awareness level and energy-monitoring app usage

Integrated energy systems (IESs) can realize the conversion and complementarity of various energy sources, which provides opportunities and challenges for the energy market. Considering that the user’s energy consumption is affected by the energy price difference, there is a problem that the new energy output in the comprehensive energy system does not fully match the user’s energy demand period. In order to solve the above problems, this paper proposes a two‐stage optimization model of “open source and reducing expenditure” to give full play to the potential of multiple energy sources on the load side to participate in demand response (DR) and combine low‐carbon technology and market mechanisms to realize the low‐carbon economic operation of the comprehensive energy system. In the first stage, a collaborative optimization strategy for electric and thermal DR is constructed from the aspect of “reducing expenditure,” a comprehensive load fuzzy DR mechanism based on the logistic function is constructed for electric load, and the load curve and time‐of‐use (TOU) energy price are optimized considering the coupling characteristics of user energy consumption, and nondominated sorting genetic algorithm (NSGA‐II) solution to achieve peak shaving and valley filling. In the second stage, a joint operation model of carbon capture power plant (CCPP) and power‐to‐gas (P2G) equipment is built from the aspect of “open source,” and the ladder‐type carbon trading mechanism is considered to rationalize the unit output and achieve low‐carbon emission reduction. The calculation results obtained through examples show that the total cost of the model is slightly reduced by 5.44%, but the actual total carbon emission of the system is greatly increased by 50.73%. It proves that the high‐carbon power plant transformation and TOU energy price optimization strategy are effective for the low‐carbon economic operation of the system and realize both economic benefits and benefits of the system.

To tackle the variability of distributed renewable energy (DRE) and the timing differences in load demand, this paper perfects the integrated layout of “source-load-storage” energy control in virtual power plants (VPPs). Introducing a comprehensive control approach for VPPs of varying ownerships, and encompassing load aggregators (LAs), a robust and cost-efficient operation strategy is proposed for VPP clusters. Initially, the influence of real-time electricity prices on cluster energy utilization is taken into account. Flexible shared electricity prices are formulated cluster-wide, based on the buying and selling data reported by each VPP, and are distributed equitably across the cluster. Following this, a flexible supply and demand response mechanism is established. With the goal of minimizing operational costs, this strategy responds to demand (DR) on the end-user side, instituting shifts and reductions in electricity and heat loads based on electricity and heat load forecasting data. On the supply side, optimization strategies are developed for gas turbines, residual heat boilers, and ground-source heat pumps to restrict power output, thus achieving economical and low-carbon cluster operations. Finally, the efficacy of the proposed optimization strategy is demonstrated through tackling numerous scenario comparisons. The results showcase that the proposed strategy diminishes operational costs and carbon emissions within the cluster by 11.7% and 5.29%, respectively, correlating to the unoptimized scenario.

Optimized operation of integrated energy systems accounting for synergistic electricity and heat demand response under heat load flexibility

Building an integrated energy system with source-load coordination and flexible interaction in the context of dual carbon is an effective path to building a new type of power system. The efficient operation of the integrated energy system can be promoted by establishing incentives such as energy subsidies to reasonably regulate the demand response on the customer side. In this paper, we consider the multi-source demand response on the customer side and study the optimal energy strategy from the perspective of the Integrated Energy Service Company (IESC), specifically involving the construction of the system electricity demand response and heat demand response models. And we propose an IESC optimal energy flow considering the customer’s demand and supply balance game to optimize the IESC. The optimal energy flow strategy of IESC considering the demand response of users is proposed. Finally, the correctness and rationality of the model are verified by simulation analysis.

Low carbon economic dispatch of integrated energy system considering extended electric heating demand response

In the new energy power system, the main applications of energy storage technology include power peak shaving, suppressing the fluctuation of transmission power, improving the stability of power system operation, and improving the power quality. The energy storage device can timely absorb or release power, effectively reduce the transmission network loss of the system, realize peak shaving and valley filling, and obtain economic benefits. Therefore, the significance of energy storage technology is very important. The use of all kinds of energy cannot be separated from the support of energy storage technology, but the traditional energy storage technology fails to optimize the configuration, resulting in low overall energy storage efficiency. Due to the low response degree of the traditional shared energy storage configuration method of the regional energy systems and the waste of resources caused by the large energy consumption of energy storage devices, the shared energy storage configuration optimization method of the regional energy systems based on electric and thermal demand response is researched. The electric heating demand response model is established to store heat and electricity, and the energy is reasonably utilized to meet the electric heating demand response. The distributed photovoltaic regional energy model is established to control the equipment and reduce the active and reactive power loss, and the cloud shared energy storage is used to optimize the configuration and reduce the cost to complete the optimization of the shared energy storage configuration of the regional energy system responding to the electric heating demand. The experimental results indicate that the proposed method has the highest energy storage capacity saving rate, realizes the efficient conversion of energy resources, reduces energy waste, and realizes the effective optimization of shared energy storage configuration in the regional energy systems.

Two-stage robust optimization for space heating loads of buildings in integrated community energy systems

Heat supply accounts for a substantial amount of terminal energy usage. However, along with price rises in primary energy, there is an urgent need to reduce the average cost of energy consumption during the purchasing of thermal services. Electric heating, an electricity-fed heating production and delivery technology, has been suggested as a promising method for improving heating efficiency, due to the ease of scheduling. However, the traditional centralized operating methods of electricity purchasing rely on explicit physical modeling of every detail, and accurate future predictions, the implementation of which are rarely practical in reality. To facilitate model-free decisions in the field of electricity purchasing, heat storage, and supply management, aimed at cost saving in a real-time price environment, this study proposes a scheduling framework based on deep reinforcement learning (DRL) and the existence of responsive users. First, the structure of a distributed heating system fed by regenerative electric boilers (REBs), which facilitate shiftable heat-load control, is introduced. A terminal heat demand response model based on thermal sensation vote (TSV), characterizing the consumption flexibility of responsive users, is also proposed. Second, due to thermal system inertia, the sequential decision problem of electric heating load scheduling is transformed into a specific Markov decision process (MDP). Finally, the edge intelligence (EI) deployed on the demand side uses a twin delayed deterministic policy gradient (TD-3) algorithm to address the action space continuity of electric heating devices. The combination of a DRL strategy and the computing power of EI enables real-time optimal scheduling. Unlike the traditional method, the trained intelligent agent makes adaptive control strategies according to the currently observed state space, thus avoiding prediction uncertainty. The simulation results validate that the intelligent agent responds positively to changes in electricity prices and weather conditions, reducing electricity consumption costs while maintaining user comfort. The adaptability and generalization of the proposed approach to different conditions is also demonstrated.

Variable heat pricing to steer the flexibility of heat demand response in district heating systems

Peer-to-peer multi-energy distributed trading for interconnected microgrids: A general Nash bargaining approach

This article presents a coordinated operation model for energy management of a multi-integrated energy system based on Mixed-Integer Linear Programing (MILP). The power derived by trains from regenerative braking energy (RBE), during deceleration, is utilised to meet the interconnected energy hubs’ (IEHs) demand. The recovered energy is calculated by simulating the motion of the trains in MATLAB software. The electricity and heat demand response (DR) programs are integrated into the proposed model to study their impacts on the operating cost and the carbon emission of the IEH, considering several case studies. Furthermore, the uncertainties of the RBE, photovoltaic power generation, and loads of the IEH are considered by formulating the optimisation problem stochastically through a scenario-based approach. Therefore, a scenario generation and reduction decision-making technique is employed. Finally, the GAMS optimisation software is used to assess the efficiency of the presented MILP model. The simulation results indicate that the total operating cost of the IEH reduced 2.0% and 1.4% in the case studies. Also, the CO2 emission is decreased by about 0.3% by applying the coordination scheme besides the DR programs.

This paper proposes a bi-level optimization framework for buildings to heating grid (B2HG) integration in integrated heating/electricity community energy systems. The proposed B2HG framework enables buildings to carry out heating demand response to the integrated community energy systems (ICES). In upper level, ICES operator with energy conversion equipment devotes to maximizing its profits by optimizing the energy generation/supply schedules and the sale heating price. In lower level, a detailed physical model of the building with adjustable indoor radiators is developed while considering the building thermal dynamics. Consumers minimize their heating costs by adjusting the flow rates of their radiators according to the heating price provided by the upper level. Then, the proposed bi-level optimization problem is converted into a mixed-integer linear programming using Karush-Kuhn-Tucker conditions and strong duality theorem. The piecewise linearization is used to eliminate the nonlinearity of heating network constraints. Numerical results show that consumers can provide heating demand response to ICES operator by considering the thermal dynamics. With the multiple energy conversion equipment, the ICES operator can unlock the flexibility from the supply side. Moreover, the proposed bi-level optimization method can combine the flexibility from both the supply and demand side, and benefits both the ICES operator and building consumers.

Recently, demand‐response (DR) programmes are one of the appropriate tools for energy systems to encourage flexible customers to participate in the operation of energy systems. One of the complex tasks in multi‐energy environments is optimal energy flow (OEF) problem of these systems. In this regard, this study investigates the OEF of an integrated electrical, heat, and gas system considering flexible heat and electrical demands. The conventional DR programme has been combined with the demand‐side energy supplying management activity by introducing switching concept among input energy carriers. The way of the supplying energy of flexible customer can be changed by switching among input energy carriers. Here, the integrated system operator minimises the system operation costs subject to supply flexible consumers’ energy. To solve the complex OEF problem, this study presents a new optimisation algorithm named modified biogeography‐based optimisation (BBO) algorithm. In this study, the proposed modification for the original BBO increases the robustness and the capability of the proposed optimisation method. The numerical results show that the flexible DR programme creates smoother energy demand curves in heat and electrical networks and reduce the operating costs of the integrated system.

Using an integrated demand-supply optimization model, this work investigates the potential for flexible space heating demand, i.e., demand response (DR), in buildings, as well as its effects on the heating demand and the operation of a district heating (DH) system. The work applies a building stock description, including both residential and non-residential buildings, and employs a representation of the current DH system of the city of Gothenburg, Sweden as a case study. The results indicate that space heating DR in buildings can have a significant impact on the cost-optimal heat supply of the city by smoothing variations in the system heat demand. DR implemented via indoor temperature deviations of as little as +1 °C can smoothen the short-term (daily) fluctuations in the system heating demand by up to 18% over a period of 1 year. The smoothening of the demand reduces the cost of heat generation, in that the heat supply and number of full-load hours of base-load heat generation units increase, while the number of starts for the peaking units decreases by more than 80%. DR through temperature deviations of +3 °C confers diminishing returns in terms of its effects on the heat demand, as compared to the DR via +1 °C.

RETRACTED: Risk-based stochastic scheduling of energy hub system in the presence of heating network and thermal energy management

Bottom-up modelling methodology for urban-scale analysis of residential space heating demand response

Combined heat and power (CHP) is an important distributed generation type for the microgrids (MGs) with both thermal and electricity demand. In this paper, a multiparty energy management framework with electricity and heat demand response is proposed for the CHP-MG. First, in order to decide the electricity and thermal prices, an optimization profit model of a microgrid operator (MGO) is formulated including the cost of gas, the income of energy sold to the consumers, and the income of surplus electricity feed to the utility grid. The CHP system is operated in a hybrid mode by dynamically selecting the following-thermal-load mode and the following-electric-load mode. Moreover, for the building energy consumers, an optimization model is formulated containing the utility of electricity consumption, the expenditure of purchasing electricity/heat, and the comfortable degree of indoor temperature. The trading process between the MGO and consumers is designed as a one-leader $N$ -follower Stackelberg game, and the existence and uniqueness of the Stackelberg equilibrium is proved. Finally, the case study of a CHP-MG system containing six building users is provided to show the effectiveness of the proposed method.

Space heating demand response potential of retrofitted residential apartment blocks