Electric Heating Loads Research Articles

Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility

This paper proposes a novel scalable type of multi-agent reinforcement learning-based coordination for distributed residential energy. Cooperating agents learn to control the flexibility offered by electric vehicles, space heating and flexible loads in a partially observable stochastic environment. In the standard independent Q-learning approach, the coordination performance of agents under partial observability drops at scale in stochastic environments. Here, the novel combination of learning from off-line convex optimisations on historical data and isolating marginal contributions to total rewards in reward signals increases stability and performance at scale. Using fixed-size Q-tables, prosumers are able to assess their marginal impact on total system objectives without sharing personal data either with each other or with a central coordinator. Case studies are used to assess the fitness of different combinations of exploration sources, reward definitions, and multi-agent learning frameworks. It is demonstrated that the proposed strategies create value at individual and system levels thanks to reductions in the costs of energy imports, losses, distribution network congestion, battery depreciation and greenhouse gas emissions.

Operation strategy for interactive CCHP system based on energy complementary characteristics of diverse operation strategies

The interactive operation of multiple combined cooling, heating, and power (CCHP) systems can improve the performance of regional integrated energy systems by exploiting the spatiotemporal complementarity of loads. The interaction of various energy networks in the interactive combined cooling, heating, and power (ICCHP) system introduces new challenges to the optimization of the operation strategy. An interactive operation strategy based on the energy complementarity characteristics of the following electric load, following thermal load, and following hybrid electric–heating load operation strategies is proposed in this paper. To investigate the interaction effect on the system performance and the effect mechanisms when it operates interactively between different users, the load heat-to-power ratio of users was first classified and abstracted. Then, the performances of the ICCHP between two users with different loads and between two users with the same loads are analyzed separately. Finally, the effect of a third user was investigated to expand the application of the interactive operating strategy to the multi-user mode. The results showed that the interaction between CCHP systems with different loads can make use of the complementarity between different loads and the energy complementarity of operation strategies, which greatly improves the performance of the ICCHP system. CCHP systems with the same loads can only rely on the energy complementarity of the operation strategies, leading to less improvement in performance. In addition, the performance of the ICCHP system will be significantly improved in the three-CCHP interactive mode if the performance of the third building is higher in independent operation.

Bi-level decentralized control of electric heating loads considering wind power accommodation in real-time electricity market

Flexible resources on demand-side, such as electric heating loads (EHLs), are perceived as an effective way of supporting the power balance of power grids with large scale renewable energy integration. However, it is difficult to quickly, effectively and evenly aggregate and schedule a large population of these loads because of their heterogeneity and geographic dispersion. The efficiency and effectiveness of optimal control are important for achieving the power balance between power source and load, especially in real-time scheduling. Moreover, the even load allocation between these heterogeneous loads also needs to motivate users participation in real-time power balance. To follow wind power in the real-time electricity market, this study presents a bi-level control method for EHLs based on distributed optimization computing, which considers the bi-level queuing method, different load control modes and multi-time scales between different control levels. First, each load group, which includes a large population of EHLs is controlled by a distributed server (DS); all the load groups are scheduled by the central management server (CMS), which deals with the wind farm or electricity retailer. Next, a distributed optimization model is built based on the bi-level control structure, in which the different power balance statuses and multi-time scales between the electricity market and EHLs are considered. Furthermore, considering the power balance on the multi-time scales, layer-by-layer load allocation optimization through the bi-level time-varying temperature queuing method, and the load-leveling optimization, serve as the sequential optimization problems. Finally, a simulation including 12,500 EHLs based on measured data in northern China is carried out. The results show that the proposed approach can effectively realize real-time power balance between wind power and EHLs, while maintaining uniform load allocation between the EHLs.

Heating and Lighting Load Disaggregation Using Frequency Components and Convolutional Bidirectional Long Short-Term Memory Method

Load disaggregation for the identification of specific load types in the total demands (e.g., demand-manageable loads, such as heating or cooling loads) is becoming increasingly important for the operation of existing and future power supply systems. This paper introduces an approach in which periodical changes in the total demands (e.g., daily, weekly, and seasonal variations) are disaggregated into corresponding frequency components and correlated with the same frequency components in the meteorological variables (e.g., temperature and solar irradiance), allowing to select combinations of frequency components with the strongest correlations as the additional explanatory variables. The paper first presents a novel Fourier series regression method for obtaining target frequency components, which is illustrated on two household-level datasets and one substation-level dataset. These results show that correlations between selected disaggregated frequency components are stronger than the correlations between the original non-disaggregated data. Afterwards, convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) methods are used to represent dependencies among multiple dimensions and to output the estimated disaggregated time series of specific types of loads, where Bayesian optimisation is applied to select hyperparameters of CNN-BiLSTM model. The CNN-BiLSTM and other deep learning models are reported to have excellent performance in many regression problems, but they are often applied as “black box” models without further exploration or analysis of the modelled processes. Therefore, the paper compares CNN-BiLSTM model in which correlated frequency components are used as the additional explanatory variables with a naïve CNN-BiLSTM model (without frequency components). The presented case studies, related to the identification of electrical heating load and lighting load from the total demands, show that the accuracy of disaggregation improves after specific frequency components of the total demand are correlated with the corresponding frequency components of temperature and solar irradiance, i.e., that frequency component-based CNN-BiLSTM model provides a more accurate load disaggregation. Obtained results are also compared/benchmarked against the two other commonly used models, confirming the benefits of the presented load disaggregation methodology.

Electric Heating Load Forecasting Method Based on Improved Thermal Comfort Model and LSTM

The accuracy of the electric heating load forecast in a new load has a close relationship with the safety and stability of distribution network in normal operation. It also has enormous implications on the architecture of a distribution network. Firstly, the thermal comfort model of the human body was established to analyze the comfortable body temperature of a main crowd under different temperatures and levels of humidity. Secondly, it analyzed the influence factors of electric heating load, and from the perspective of meteorological factors, it selected the difference between human thermal comfort temperature and actual temperature and humidity by gray correlation analysis. Finally, the attention mechanism was utilized to promote the precision of combined adjunction model, and then the data results of the predicted electric heating load were obtained. In the verification, the measured data of electric heating load in a certain area of eastern Inner Mongolia were used. The results showed that after considering the input vector with most relative factors such as temperature and human thermal comfort, the LSTM network can realize the accurate prediction of the electric heating load.

A research on integrated district energy system planning considering hydrogen production

In this paper, the comprehensive energy system of wind power hydrogen production plant is considered. Firstly, wind power hydrogen production model, electrolysis hydrogen production model and comprehensive energy system model containing wind power hydrogen production are proposed, combining the operation model in the comprehensive energy system, electric heating load demand and the operation characteristics of the power system. Secondly, the objective function of the integrated energy system is to optimize for the lowest total operating cost, establish an optimization model, and restrict its overall power balance and the relevant constraints of each part. Finally, energyplan is adopted for simulation operation. The results show that the integrated energy system considering wind power for hydrogen production can better absorb wind energy, reduce the phenomenon of wind abandonment, and convert wind abandonment into hydrogen resources, which can improve economic benefits and make full use of energy.

Research on Power Demand Response of A Certain Area Power Grid for New Energy Consumption

With the rapid growth of new energy installation scale, new energy consumption is facing more and more pressure and challenge. Based on the characteristics of a certain provincial power grid, this paper starts from the point of view of promoting clean energy consumption, guiding electric heating load and other adjustable loads to optimize electricity consumption, relieving peak-shaving pressure of power grid, and strengthening the emergency regulation ability of the certain power grid, the power demand response of provincial power grid is designed and realized. This mode and methods have universal application value for provincial power grid with high proportion of new energy installation.

Effect of Full Implementation of Domestic Solar Water Heaters on the Electricity Peak Load in Libya

Electricity plays an important role in the contemporary life, and it has become indispensable nowadays. Reducing the peak electricity load and increasing the load factor have been considered as one of the main tasks that have to be accomplished by both electricity generation-side and demand-side managements.The residential sector of Libya consumes over 31% of the total sold electricity, and 29.8% of that is delivered to the electric water heating load. This is an inefficient way of electricity utilization. Usually, the electricity supplier in Libya used to increase the local generation capacity or import electricity from neighboring countries. Both solutions did not resolve the problem. This work attempts to investigate the effect of replacing electric water heaters in the residential sector of Libya by solar water heaters on reducing the electricity peakload and increasing the load factor. The results show that on average 3% of the peak load demand can be saved. This is equivalent to 149.5 MW of reduced power. The study also revealed that the annual amount of energy saved is up to 2.55TWh, and the load factor is improved by 2% (i.e. from 65% to 67%). This saved energy is equivalent to a power plant with a nominal capacity of 448 MW considering a load factor of 0.65

Optimization Strategy of Electric Heating with Load Regulation Ability

In recent years, more and more users choose electric heating to replace traditional centralized heating mode in the northern area. The flexibility of electric heating, the energy consumption characteristics of buildings, the regulation ability of users and Time-of-Use (TOU) electricity tariff give much possibility to optimize the output power strategy of electric heaters. The existing electric heating control system, where the suitable working temperature is usually used as the only set value for human life and working environment, leads to unnecessary energy consumption in some low heat demand area.This paper, focused on a commercial used building in Shandong province, takes different heating demand characteristics into consideration, establishes electric heating load model and analyses each load’s regulation ability. Six distributed electric heaters are installed in the building, with one room equipped with one electric heater. The heater is modelled based on the parameters and output power characteristics. For the building itself, the energy consumption characteristics and thermal inertia are also analysed and presented.

Optimization of electric heating operation based on day-ahead electricity spot market

The emergence of Electricity Spot Market makes it possible that distributed electric heating equipment connect with the grid and participate in market competition as well. However, due to the functional limits, it is impossible for the power supply side to have a thorough understanding of the specific conditions of the load characteristics of the power user, and the user side will not take a global consideration of the real-time security and stability of the power grid. This paper focuses on the research about electricity consumption strategy of thermoelectric coupled load with the background of electricity spot market. The main works are as following: An operation strategy of consumer electric heating equipment based on electric spot market is proposed. Besides, an operation mode which responds to the market is obtained by analysing the time span of clearing nodes in power spot market and start-stop characteristics of distributed heat load equipment. Based on this, the operating cost control function of consumer electric heating load is designed, making it possible that the load follows changes of the clearing price under the premise of ensuring that the user’s heat demand is met within a day. Then we had a discussion about the impact of parameter changes on the operating strategy. With the help of the constraints of existing power grid, the operation strategy model of consumer electric heating equipment based on power spot market is realized.

Day-ahead Optimal Scheduling of Regenerative Electric Heating System Considering Load Imbalance

For the regenerative electric heating system, on the premise of ensuring reliable heat supply to users, a day-ahead optimization scheduling method for the regenerative electric heating system considering the load imbalance is proposed. First, users' heating demand under normal working conditions and grid power rationing scenarios are calculated by estimate index method. Then, in order to match the heating demand of users and reduce the load imbalance caused by thermal storage electric heating in the distribution network, comprehensive consideration of grid constraints and the adjustable capacity of regenerative electric heating load, the operating strategy of the thermal storage electric heating system is studied. Reasonable control of heat storage and release in regenerative electric heating can not only reduce the distribution line pressure during heating period, but also maximize the accommodation of low-cost electricity such as surplus renewable energy and improve the economic benefits of the system. Taking the regenerative electric heating system in Chongli area of Zhangjiakou, Hebei Province as an example, the multi-objective optimal scheduling model is simulated and analyzed, and the feasibility and effectiveness of the proposed optimal scheduling strategy are verified.

Evaluation and Control Strategy of the Regulation Capacity of Electric Heating Load

In face of increasingly severe environmental problems, green and low-carbon power supply has become an important trend of current development. At present, the smart grid which will respond to the demand has improved the interaction between current users and the grid. In addition, electric heating and solar power generation and other renewable power generation methods are promoted in north China. Electric heating is a flexible and regulatable load; when it reaches a certain scale, it is bound to become an objective demand response resource in power grid operation, so it is of great practical significance to explore the evaluation of the regulation capacity of electric heating load.

Optimal scheduling of Park level integrated energy system considering electrothermal coupling

Considering the inherent characteristics of the park heating load, such as transmission delay, fuzzy heating comfort, etc., it can be used as a flexible load to participate in the optimal scheduling. Aiming at the minimum operation cost of the integrated energy system in the park, a collaborative optimal scheduling model of the park's integrated energy system with the participation of comprehensive demand response of electric heating load is constructed. The simulation results show that, compared with the optimization results of traditional power demand response, the application of integrated demand response of electric heating load improves the flexibility of production of cogeneration units in the park, reduces the total energy consumption cost of demand side users and the operation cost of the system on the premise of ensuring the balance of supply and demand of the system, improves the energy utilization efficiency, and realizes the environmental protection and economy of the system function.

The evaluation of the thermal storage electric heating load adjustability

In recent years, in order to solve the environmental pollution problem caused by coal-fired heating in the heating area of north of china, electric heating has received extensive attention and has gradually been developed vigorously. Considering the building characteristics, outdoor temperature, indoor comfort and other factors, the building heat load adjustability model is established, and the heat storage device's load adjustability model considering the current operating status and heat storage capacity of the heat storage device is established, and the two are added together An evaluation model of the adjustability of regenerative electric heating users' load is obtained. This model not only considers the user comfort and the adjustment potential of the building where it is located, but also considers the adjustment potential of the heat storage device, which can more comprehensively and accurately evaluate the user load adjustment potential of thermal storage electric heating.

Modeling of regional electrical heating load characteristics considering user response behavior difference

The load characteristics of “coal-to-electricity” based on demand response are affected by the users’ diversified demand and differential response behavior. Therefore, to describe the response behavior of different users in detail, a regional electric heating system load characteristics modeling method considering the dual difference of demand and response behavior is proposed. Firstly, considering the users’ characteristics and external environmental factors, a thermal comfort temperature model of differential users is proposed. And a thermal load characteristics modeling method is proposed based on the air temperature time-varying equations of buildings. Secondly, a multi-objective optimization method and a decision-making method are used to simulate the differential response behavior of users to obtain the load characteristics of a single class of users, which based on the discrete selection model. Finally, the regional users’ classification criteria are established, which based on the influence factors of heat demand and response behavior. And the modeling method of multi-type users aggregate load characteristic is proposed. At the end of this paper, case studies of the modeling method have been validated the effectiveness of improving the solution accuracy of load characteristics.

Distributed Energy and Thermal Management of a 48-V Diesel Mild Hybrid Electric Vehicle With Electrically Heated Catalyst

The 48-V diesel mild-hybrid electric vehicle equipped with an electrically heated catalyst is a cost-effective solution to reduce tailpipe emissions, while its supervisory control design is more challenging, due to cross-coupled energy and thermal subsystems. This article presents a distributed control design for the energy and thermal management system, aiming to optimize fuel economy and reduce catalyst warm-up time, by cooperation between electrical heating and engine load upshift. A control-oriented model of HEV and catalyst is developed, which captures the dynamics of the battery state of charge and catalyst temperature. The energy and thermal management problem is cast as a hybrid optimal control problem, to minimize entire trip fuel consumption under different switching (warm-up) times. An off-line solution method is designed to exploit the tradeoff between fuel and warm-up time based upon hybrid minimum principle and a nested shooting algorithm. For online implementation, a distributed supervisory control is proposed, which covers both cold-start and normal operations, and is compatible with the equivalent consumption minimization strategy. Both off-line and online methods are validated in simulation, using two driving cycles and five warm-up speeds.

Design and Performance of a Parabolic trough System for Process Heat Application

The worldwide energy demand is rapidly increasing, increasing in turn the necessity of power generation. The generated power is used to fulfill domestic and industrial needs. Industries use electricity for medium scale heating applications, which is the worst use of electricity. Concentrated solar power can prove itself a superior alternative of electrical heating load, being the only technology which produces high quality heat. In this paper, a systematic design approach of a parabolic trough based solar field for a defined thermal load is presented. The performance simulation of the field has been done for a small scale, medium temperature industrial drying process of 290 kWth at 120oC, located in Bhopal using System Advisor Model (SAM), was found to be 23% efficient. The results can be used as a roadmap for designers for the feasibility analysis of local industrial application

Research on market trading mechanism of distributed photovoltaic and electric heating load

Based on the analysis of the electricity sale mode of distributed photovoltaic and the electricity consumption trading mode of electric heating load, a market-oriented trading mechanism model of distributed photovoltaic and electric heating load is proposed, which provides a theoretical basis for the online analysis and operation control of distribution network under the market-oriented trading.

Research on Control Strategy of Electric Heating Load Cluster

At present, due to the rapid development of the economy and the unceasing enthusiasm of the modernization process, human beings are increasingly dependent on electric energy. The development of renewable energy and energy efficiency have become the consensus in the energy field. The randomness of in the future solar power generation, wind power and other intermittent and new energy sources in the power grid continue to rise, which is bound to occupy an increasingly important position power energy structure. The active regulation capability of the “power supply side” in a high-ratio renewable energy grid is degraded. To ensure safe and efficient operation of the grid, it is imperative to develop the regulation capability on the load side. This paper establishes a cluster aggregation model, clusters the electric heating load through scientific and rational grouping, makes the electric heating load form a load group for unified regulation, formulates an appropriate cluster optimization control strategy, and makes full use of the cluster electric heating control potential to achieve peak clipping. Fill the valley, effectively improve the regulation capacity of the grid.

Joint planning of distribution network and substation considering the effect of electric heating load

A joint planning method for distribution network and substation considering the influence of electric heating load is proposed based on LCC in this paper. A multi-state model of electric heating load level is obtained by state sampling combination of ordinary load and electric heating load according to Monte Carlo method (MCS). Taking line investment cost, operation and maintenance cost, adjustable capacity transformer investment cost, operation cost, network loss cost and user outage loss cost as objective functions, a joint planning model of distribution network and substation considering the influence of electric heating load is established. The multi-objective optimization problem is transformed into a single-objective optimization problem by weighting, and solved by genetic algorithm with mixed chromosome coding. The simulation results show the effectiveness of the proposed method.