Aggregate Load Research Articles

A regulation capability enhancing strategy for controllable loads based on relay control

The contemporary power system features diversified and distributed loads with significant regulation potential. In heating areas, electric heating loads are prevalent but underutilized due to comfort constraints. To address this problem, the paper proposes to utilize relay combined control to deeply exploit the regulation potential of electric heating loads. To ensure the comfort of users while exploiting the potential of electric heating loads as much as possible, the regulation characteristics of electric heating loads are firstly studied and analyzed, and the regulation capacity of electric heating load groups is evaluated through the load aggregator (LA) to participate in the grid regulation by group aggregation of electric heating. According to the characteristics of the regulation capacity distribution, the influencing factors of the regulation capacity of electric heating are analyzed. Secondly, by employing the relay control strategy, the control strategy’s efficacy is enhanced by orchestrating adjustable electric heating loads based on their regulation characteristics. Finally, the simulation comparison before and after adopting the control strategy is carried out under different conditions by means of an example, and the exploitation rate of the regulating capacity is increased by nearly 50%, which verifies the feasibility and effectiveness of the proposed control strategy.

Optimized scheduling of smart community energy systems considering demand response and shared energy storage

Integrated energy systems within communities play a pivotal role in addressing the diverse energy requirements of the system, emerging as a central focus in contemporary research. This paper contributes to exploring optimal scheduling in a smart community featuring multiple smart buildings equipped with a substantial share of distributed photovoltaic sources, shared energy storage, and controllable loads. The study formulates a two-stage scheduling optimization model incorporating multiple stakeholders, explicitly examining the game dynamics between the smart community operator and the customer load aggregator. The weights assigned to each optimization objective are determined using an analytic hierarchy process-entropy weight method to establish a balanced and nuanced approach. The ensuing optimal scheduling encompasses unit output and energy trading considerations, focusing on enhancing the system's economic viability, safety measures, and environmental sustainability. A comprehensive evaluation is conducted to validate the proposed model by comparing its performance with alternative operational strategies. The results show that the designed strategy can reduce the operating cost by 40.22% and increase the level of PV consumption by 22.57% compared to the traditional heat-based strategy, which is effective in mitigating power fluctuations, smartly responding to dispatch peak demand, enhancing new energy integration, and ensuring the security of grid operation.

Multi-agent low-carbon optimal dispatch of regional integrated energy system based on mixed game theory

In the context of global warming and energy scarcity, regional integrated energy system (RIES) can effectively improve energy utilization efficiency and reduce carbon emissions. However, this leads to the increased complexity of the market transactions. To this end, based on the mixed game strategy, a multi-agent low-carbon optimal dispatch model of RIES is proposed in this paper. Firstly, to fully consider the low-carbon objective of the system, a reward and punishment ladder carbon trading mechanism and a ladder integrated demand response (IDR) model are developed. Then, the decision models of the energy management operator (EMO), multiple energy hub agents (EHAs) and the user load aggregator (ULA) are constructed respectively with the goal of economy, and carbon emission flows are introduced as constraints. The interactive trading process of various stakeholders is simulated by a mixed game with master-slave game nested bidding strategy. Finally, the economics and carbon emissions of the system in different scenarios are analyzed by examples, and the effectiveness of the proposed model is verified. Numerical simulation results show that the proposed method not only has good economic and low-carbon environmental protection benefits, but also ensures the interests of various stakeholders.

Bidding Strategies of Load Aggregators for Day-Ahead Market With Multiple Uncertainties

Bidding Strategies of Load Aggregators for Day-Ahead Market With Multiple Uncertainties

Towards a Sustainable Power System: A Three-Stage Demand Response Potential Evaluation Model

Developing flexible resources is a key strategy for advancing the development of new power systems and addressing the issue of climate change. Demand response is a crucial flexibility resource that is extensively employed due to its sustainability and economy. This work develops a three-stage demand response potential evaluation model based on “theoretical potential–realizable potential–multi-load aggregation potential” in response to the issues of inadequate consideration of numerous complicated agents and time in previous research. Firstly, the traditional method calculates the theoretical maximum demand response potential of a single industry in each period. Based on this, the industry characteristics are taken into account when establishing the demand response potential evaluation model. Lastly, the time variation of the demand response potential is taken into consideration when evaluating the demand response potential of multiple load aggregation. For the analysis, three industries are chosen as examples. The results show that the potential of peak shaving and valley filling obtained by using the model is smaller than that of the traditional method, the reduction range of peak cutting demand response potential calculated by multi-load aggregation is 19–100%, and the reduction range of valley filling demand response potential is 20–89%. The results are closer to reality, which is conducive to improving the accuracy of relevant departments in making relevant decisions and promoting the sustainable development of a new power system.

Optimal dispatching strategy for residential demand response considering load participation

To facilitate the coordinated and large-scale participation of residential flexible loads in demand response (DR), a load aggregator (LA) can integrate these loads for scheduling. In this study, a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR. First, based on the operational characteristics of flexible loads such as electric vehicles, air conditioners, and dishwashers, their DR participation, the base to calculate the compensation price to users, was determined by considering these loads as virtual energy storage. It was quantified based on the state of virtual energy storage during each time slot. Second, flexible loads were clustered using the K-means algorithm, considering the typical operational and behavioral characteristics as the cluster centroid. Finally, the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load. The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys, thereby improving the load management performance.

Evaluation of regression models and Bayes-Ensemble Regressor technique for non-intrusive load monitoring

Non-Intrusive Load Monitoring (NILM) is referred to as the task of decomposing the aggregated power load of a residential or commercial building into appliance-level consumption without the installation of dedicated smart meters. NILM, mostly considered as a supervised learning problem, is crucial for energy monitoring and management as this approach can detect load malfunction and prevent wastage of energy consumption. For most of regression based NILM research, not much work has been done in analyzing data against temporal parameters such as day of the week, week of the month, week of the year, and various quarters in a year. Additionally, various NILM research works focus on finding the best regression model for predicting appliance consumption but discards the fact that appliance usage varies on factors such as seasons, different working hours, and weekends. This paper aims to evaluate some regression algorithms used towards NILM research based on 8 different training and testing Methods which according to our knowledge covered major factors that affect the appliance usage. The dataset used for the evaluation of the regression models, were collected from a research lab at Grenoble INP, in Grenoble, France. The evaluation results show that instead of one algorithm, individual regression algorithms generated favorable outcomes for various appliances based on different train-test Methods. Furthermore, a novel Bayesian optimized Ensemble regressor model for predicting individual appliance consumption from aggregated load data is also proposed. Instead of just using the aggregated power information, the proposed model also uses temporal information of the dataset to estimate accurate consumption output of individual appliances. Extensive simulation results corroborate the merits of the proposed approach, which outperforms the benchmarking Methods. To ensure reproducibility of the results by the research community and a potential future improvement of the framework by other researchers, the complete source code is provided in the following repository: https://github.com/Mohammad-Kaosain-Akbar/NILM-Ensemble-Bayesian-Optimization

Self-Supervised Feature Learning for Appliance Recognition in Nonintrusive Load Monitoring

Non-intrusive load monitoring (NILM) can monitor the operating state and energy consumption of electric appliances in a non-intrusive manner and provides a promising approach to improving electricity usage efficiency for residential and commercial buildings. Although machine learning (ML) methods are powerful and have significantly advanced the developments of NILM, they request a sizable amount of labelled data for model training. However, getting operational data of each elec- trical appliance in real life is challenging, so the requirements for labelled data limit the NLIM's practicality. To tackle this challenge, a novel multi-layer momentum contrast (MLMoCo) learning mechanism is proposed for self-supervised feature rep- resentation learning. With only unlabelled aggregate load data, the proposed MLMoCo contrasts the augmented versions of the same sample (“positives”) with instances extracted from other samples (“negatives”). To maintain a dictionary with enough negative samples to be compared with the input, a momentum encoder is adopted to momentum update the parameters rather than by backpropagation during training. An event-based data augmentation method is also proposed to obtain the distinct but strongly related positive pairs for self-supervised feature learning. The experimental comparisons, including different state-of-the- art techniques and various downstream tasks with real-world datasets, demonstrate the remarkable performance gains of the proposed approach through learning from the unlabelled data, which could significantly increase the practicality of the NILM.

Aggregated demand-side response in residential distribution areas based on tiered incentive prices

The residential area refers to the power supply area from distribution transformers to the end users that contains multiple types of flexible resources, such as photovoltaics, energy storage, and power users. Focusing on the challenge of insufficient demand response incentives to multiple types of users in residential distribution areas, a tiered incentive price-based demand-side aggregated response method is proposed in this paper. Users in residential distribution areas are classified with an improved k-means clustering method for obtaining typical types of users. Thereafter, initial scores of users are calculated, and their grades are assigned based on their scores. Corresponding tiered incentive prices are designed for different grades. On this basis, a leader–follower game is proposed to obtain the demand response base price, and tiered incentives are provided to users of different grades to increase their enthusiasm for participating in demand response. In the case study, an actual urban residential distribution area is studied. The results show that the proposed user clustering method has an accuracy of 99.8% in classifying users in a residential distribution area. In addition, the proposed method has better performance in terms of improving the benefit of the load aggregator and users in the residential distribution area compared with methods such as potential game, hidden Markov, and Monte Carlo. Specifically, from the results, the benefit of load aggregators is increased by 101.96%, 76.07%, and 112.37%, and the income of the users is increased by 54.51%, 36.94%, and 64.91%.

Research on bi‐layer low carbon scheduling strategy for source‐load collaborative optimization based on node carbon emission intensity

AbstractTo accurately calculate the carbon emission of integrated energy system (IES), and fully explore the potential for load side on carbon emission reduction, this paper proposes a method of guiding load to participate in demand response based on node carbon emission intensity, and constructs a bi‐layer low‐carbon scheduling model for source‐load collaborative optimization. First, the carbon flow calculation model of IES in the total process is established, such as source, network, load, and storage. It can depict the carbon emission characteristics of energy conversion equipment and energy storage devices. The principle of proportional sharing is used to track carbon emission flows, the changes in carbon emission intensity at each node is perceived from a spatiotemporal perspective. Second, carbon flow analysis is incorporated into the load demand response mechanism, and a carbon emission model for load aggregator (LA) after demand response is established based on the node carbon emission intensity. Third, a bi‐layer low‐carbon scheduling model is constructed, which considers the source‐load collaborative optimization. The upper layer is the optimal economic dispatch of IES operators, while the lower layer is the optimal economic dispatch of LAs. Finally, the effectiveness of the proposed method is verified using the system as an example, such as improved 14‐node power grid, 6‐node heating network, and 6‐node nature gas network.

Demand Response Strategy Based on the Multi-Agent System and Multiple-Load Participation

In order to improve the utilization of user-side power resources in the distribution network and promote energy conservation, this paper designs a distributed system suitable for power demand response (DR), considering multi-agent system (MAS) technology and consistency algorithms. Due to the frequent changes in the power system structure caused by changes in the load of a large number of users, this paper proposes using cluster partitioning indicators as communication weights between agents, enabling agents to utilize the distribution network for collaborative optimization. In order to achieve the integration of multiple load-side power resources and improve the refinement level of demand-side management (DSM), two types of agents with load aggregator (LA) functions are provided, which adopt the demand response strategies of Time-of-Use (TOU) or Direct Load Control (DLC) and model the uncertainty of individual device states using Monte Carlo method, so that the two typical flexible loads can achieve the target load-reduction requirements under the MAS framework. The research results demonstrate that this method achieves complementary advantages of the two types of loads participating in DR on a time scale, reducing the costs of power companies and saving customers’ electricity bills while peak shaving.

Online transfer learning-based residential demand response potential forecasting for load aggregator

Accurate demand response (DR) potential forecasting is the basis for load aggregators (LA) to make optimal bidding strategies in DR market trading. LAs usually face practical challenges when they perform forecasts for those new customers who have no historical response data. Transfer learning provides a promising solution to this problem by leveraging knowledge acquired from other existing contracted customers. However, traditional transfer learning methods are trained offline and cannot make use of the latest response information of these new customers, which may result in large forecasting errors since the response behavior of new customers usually dynamically changes. To address the above issues, this paper proposes an online transfer learning-based DR potential forecasting framework, in which two forecasting models are established. The first one is built using the historical data of existing customers and this model is then transferred to the target domain (i.e., new customers) by parameter sharing and fine-tuning. The second model is built using the local response data of new customers, which gradually accumulates with the increasing participation of DR events. These two models are combined by an adaptive ensemble framework based on their online performances, thus enabling it to dynamically track the changes in new customers' response behavior. Case studies on a real-world dataset validate the effectiveness of the proposed framework.

Benefit Evaluation of Carbon Reduction and Loss Reduction under a Coordinated Transportation–Electricity Network

With the extensive promotion of new energy vehicles, the number of electric vehicles (EVs) in China has increased rapidly. Electric vehicles are densely parked in garages, which means parking garages contain a large amount of idle energy storage resources. How to make this idle energy storage in garages participate in power system dispatch and evaluate the network loss and system carbon emissions considering electric vehicle energy storage has become an important research topic. The uncertainty around parking habits for electric vehicles causes it to be difficult to predict compared with the traditional energy storage system. Therefore, it is necessary to study its influence on the synergistic effect of loss reduction and carbon reduction as energy storage access. The benefits of new energy power generation output growth, energy waste reduction, and carbon emission reduction brought by loss reduction measures can be well reflected in the loss reduction index system of a power system in a low-carbon scenario. In this paper, a large amount of parking information in a certain area is collected, and the approximate parking habits of all vehicles in the simulated garage are obtained by the Monte Carlo method. Then, the load aggregation model is established, which is incorporated into the power system as an energy storage model. The synergy of loss reduction and carbon reduction is considered in this paper and comprehensively optimizes the strategy of integrating electric vehicles into the power system from the perspectives of electricity and carbon. In the scenarios of carbon flow calculation and network loss calculation, the YALMIP and CPLEX of MATLAB are applied, with various constraints input for simulation, so that the benefit evaluation method of carbon reduction and loss reduction under a coordinated transportation–electricity network is obtained.

Low-carbon joint dispatch of integrated energy systems based on differentiated dynamic pricing mechanisms

World is facing the severe crisis of energy supply and great pressure of carbon emissions reduction. The development of an integrated energy system (IES) is a way to meet the goal of carbon peaking and carbon neutrality. To ensure the coordination of economic benefits of source and load sides in the low-carbon dispatch, an internal multi-stakeholder’s joint optimal dispatch model of an IES considering differentiated dynamic pricing mechanisms is proposed. A joint optimization coordination center (JOCC) is established responsible for differentiated dynamic pricing and joint optimal dispatch of an IES. The JOCC sets the selling prices of electricity and heat from different sources by introducing the concept of additional carbon emission price of ladder-type for non-clean units. The JOCC is then responsible for the information interaction of the integrated energy operator (IEO) on the source side and the load aggregator (LA) on the load side. In addition, the JOCC sets the joint optimization objective function to determine the optimal dispatch strategy which realizes the coordination of economic benefits of source and load sides. CPLEX was used to solve the mixed integer linear programming (MILP) model. Case studies verified that the proposed joint optimal dispatch model of an IES considering differentiated dynamic pricing mechanisms can endow users with the right to choose energy from different sources and improve their enthusiasm to participate in integrated demand response (IDR), that realizes the coordination of economic benefits of multi-stakeholders on the basis of ensuring the low-carbon characteristics of the IES. Compared with the time-of-use pricing strategy, carbon emissions are reduced by 2.41%, wind curtailment is reduced by 49.31%, and the total cost of LA is reduced by 26.53%.

Dynamic Aggregation Method for Load Aggregators Considering Users’ Deviation Electricity

Constructing a new energy-based power system is not only an important direction for the transformation and upgrade of China’s power system, but also a key to achieving peak carbon and carbon neutrality. How to fully utilize situation awareness technology to adapt to diverse and differentiated scenarios has become a crucial breakthrough point for ensuring the reliable, safe, high-quality, low-carbon, and economical operation of the new power system. Starting from the distribution network demand resources, this paper proposes a dynamic aggregation method for load aggregators considering the user deviation quantity, to deal with the current situation that the adjustable load-side resource points are multi-faceted and wide, and the operating subjects are complex and difficult to participate directly in the grid dispatch. First, considering there is subjectivity in the electricity behavior of users under the jurisdiction of the load aggregator, a deviation amount may be generated during the actual aggregation process, which reduces the profit of the load aggregator. Therefore, a load aggregator-level user deviation dynamic volume forecasting method based on the Markov chain is proposed, which is used to predict the deviation quantity of users during the dispatch cycle and achieve a dynamic status estimate on the load side of the new power system. On this basis, a dynamic aggregation model for load aggregators based on the deviation volume was constructed with the objective of maximizing the revenue of load aggregators. The examples, by comparing the aggregation results of users under three scenarios, show the proposed method can effectively guarantee the income of load aggregators, verify the effectiveness of the proposed dynamic aggregation strategy, and provide technical support for the operation situation awareness of the load side of the new power system.

Stackelberg-Nash asymmetric bargaining-based scheduling optimization and revenue-allocation for multi-operator regional integrated energy system considering competition-cooperation relationship and source-load uncertainties

The integration of source-grid-load-storage greatly improves the energy managing flexibility of regional integrated energy systems (RIES). Considering the conflicting interests of multi-operator, this paper divides RIES into 1) energy supply center (ESC); 2) load aggregator (LA); 3) energy storage aggregator (ESA), and establishes a multi-operator two-layer optimal scheduling model by combining 1) the Stackelbelberg-Nash asymmetric bargaining game based on the genetic algorithm; and 2) the alternating direction method of multipliers algorithm (GA-ADMM). Firstly, considering the absence of historical data, trapezoidal fuzzy parameters are introduced to construct fuzzy chance constraints based on credibility theory to simulate the source-load uncertainties. Secondly, the uncertainty of demand response behavior, real-time carbon credits trading within RIES, and cooperation-competition behavior of followers (LA and ESA) are considered to simulate the asymmetric relationship between followers and leaders (ESC) based on GA. Then, based on the ADMM algorithm, a Nash asymmetric bargaining game considering energy dependency and energy supply-reception status is proposed to realize the revenue allocation between followers. Finally, simulations are implemented to verify the validity and reliability of the proposed model. Results show that: 1) Cooperation improves the energy-managing flexibility of followers, reduces their dependence on ESC, and thus enhances their game ability. 2) The real-time carbon credits trading enables zero-carbon energy interactions and natural gas replenishment within RIES. 3) Increases in the price of various energy and carbon credits can enhance cooperation between followers.

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 %.

Incentive Compatible Pricing for Enhancing the Controllability of Price-Based Demand Response

Price-based demand response is widely implemented by the load aggregators (LAs) to exploit the flexibility of massive demand-side resources. However, a key problem in real-life implementation is the controllability of price-based demand response, viz, whether the price can guide the demand to the desired value. To bridge this gap, this paper proposes a close-loop reverse Stackelberg game-based pricing method to better regulate the demand response. In the hierarchical game structure, the non-cooperative competition of heterogeneous demand-side resources in participating demand response constitutes the lower-level, while the determination of pricing policy by LA makes the upper-level. Towards a computationally efficient solution, the mean-field game is adopted to approximate the Nash equilibrium of the large-scale lower level competition, and the indirect formulation of close-loop reverse Stackelberg game is adopted. The accuracy of the approximation is proved to be the (1,2) -hierarchical equilibrium of the original game. Finally, a decentralized solution algorithm with sequential linear programming as outer-loop and mean-field iteration as inner-loop is proposed to price the demand response. Numerical tests on a retail electricity market containing 15,000 heterogeneous demand-side flexible resources validate the effectiveness of the proposed pricing scheme.

Validation of the Energy Demand Load Profile Estimator “PROFet” for Trondheim Non-residential Buildings

Accurate long-term forecasts of aggregate energy load profiles are crucial for effective energy system planning at regional and national scales. This study aims to validate PROFet, a flexible load profile modeling tool. PROFet forecasts weather-dependent heating and electrical load profiles at an hourly resolution for both residential and non-residential buildings connected to district heating systems. Given that the tool’s accuracy for residential buildings has been demonstrated in previous studies, further validation is needed for non-residential buildings. To achieve this, our study evaluates PROFet’s extrapolation performance using an out-of-sample test dataset from Trondheim municipality. The results show that PROFet accurately forecasts the hourly heating load during the space-heating season but is less accurate during periods when domestic hot water needs dominate. The tool exhibits nearly identical performance across the three cases, except for efficient kindergartens, where the estimation accuracy is lower.

Energy, economic and environmental benefits of Demand Response for improving building energy flexibility

The increasing penetration of intermittent renewable sources in power generation at local and building-level poses growing issues in balancing generation and demand. To avoid imbalances, it is therefore necessary to ensure adequate levels of flexibility in the building energy system. This can be done both on the generation side, through the coupling of different energy carriers (cogeneration, power-to-heat solutions) and/or the integration of storage systems, and on the demand side, through smart “demand response” programs. This paper considers a tourist facility located in central Germany as a case study to evaluate the energy, economic and environmental benefits that can be obtained from the application of appropriate demand response strategies. The electrical demand data of the facility are monitored at both aggregate and individual load levels and made available by means of a cloud platform. The facility includes two stationary combined heat and power internal combustion engines powered by natural gas and a photovoltaic system. The results show how, thanks to appropriate load management, it is possible, on the one hand, to increase the self-consumption of PV-generated energy and, on the other hand, to keep more constant the load of the engines, which can therefore operate with better efficiencies. This results in both a reduction in energy expenses and a decrease in carbon dioxide emissions attributable to the building.