Aggregate Load Research Articles

Aggregate load profile and decarbonization: Impacts of actionable demand drivers in New York

In a decarbonized future, electricity will likely play a much greater role in all aspects of the economy, while power generation will increasingly rely on intermittent sources such as wind and solar. In deep decarbonization studies of power systems, the most striking conclusions are usually on the generation side: the need of tens of GW of new clean generation capacities and storage are typical conclusions. However, these conclusions are the direct consequence of electricity demand projections, which usually get less attention and sometimes lack detailed explanation. At the very least, these projections are aggregated and suffer from a deficit of transparency in their methodology. A detailed knowledge of possible future load profiles, and how the evolution of their drivers influences their shape, is therefore very important to plan adequately energy and power requirements, as well as regional interconnections and storage. This paper uses a bottom-up approach based on the useful energy consumed in the residential, commercial, transportation and industrial sectors to explore the load profile sensitivity to key parameters. This approach is then used to provide hourly load profiles for a full year for the state of New York as an illustration. The impact of these key parameters is demonstrated through three scenarios: business as usual, accelerated electrification and energy efficiency breakthrough. Policy makers, planning for very ambitious penetration level of renewable energy generation capacity have to take into account the effect on electricity peak and total demand of their energy efficiency initiatives.

Stackelberg-Game-Based Demand Response for Voltage Regulation in Distribution Network with High Penetration of Electric Vehicles

With the development of the economy, electricity demand continues to increase, and the time for electricity consumption is concentrated, which leads to increasing pressure on the voltage regulation of the distribution network. For example, a large number of electric vehicles charging during a low-price period may cause the problem of under-voltage of the distribution network. On the other hand, the penetration of distributed power generation of renewable energy may cause over-voltage problems in the distribution network. This study proposes a Stackelberg game model between the distribution system operator and the load aggregator. In the Stackelberg game model, the distribution system operator affects the users’ electricity consumption time by issuing subsidies to decrease the frequency of voltage violations. As the representative of users, the load aggregator helps the users schedule the demand during the subsidized period to maximize profits. Case studies are carried out on the IEEE 33-bus power distribution system. The results show that the time of the subsidy can be optimized based on the Stackelberg game model. Both the distribution system operator and the load aggregator can obtain the optimal economic profits and then comprehensively improve the operating reliability and economy of the power distribution system.

Demand Response Aggregation With Operating Envelope Based on Data-Driven State Estimation and Sensitivity Function Signals

With the increasing penetration of renewable energy sources (RES), the value of the demand response (DR) draws wide attention. In order to realize the coordinated dispatch of widely spread resources, the aggregation of the controllable residential loads is managed by a single entity, namely the DR aggregator. Under the price-based DR programs, the DR aggregators actively respond to the market signals to reach maximum welfare. To avoid the quality of electricity services being jeopardized, the operational constraints of the network should be considered by the DR aggregators. However, DR aggregators are not expected to have access to the monitoring equipment and have limited knowledge of the network states. Hence, in this paper, we proposed a DR aggregation with the operating envelope framework based on the representative signals produced by the distributed network operator (DNO) in the context of big data era. The DNO provides representative signals, including real-time state estimation and sensitivity functions, to the DR aggregators based on the proposed Semi-supervised Coupled Generative Adversarial Imputation Network (SC-GAIN) and big data analysis. The DR aggregators can realize the secure and efficient real-time dispatch of the controllable loads based on the received signals. The proposed framework was verified on the IEEE 33-bus and 123-bus systems. The case studies show that the proposed SC-GAIN algorithm can better deal with the missing data, and the learned sensitivity functions can effectively avoid the overestimation of the true DR potential.

SAMNet: Toward Latency-Free Non-Intrusive Load Monitoring via Multi-Task Deep Learning

Non-intrusive load monitoring (NILM), including state detection and energy disaggregation, aims to identify the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on/off</i> state and energy consumption from the aggregate load of a building. By monitoring the electrical behavior of consumers, smart grid applications such as demand response and recommendation services can also be realized for saving energy bills, environmental effectiveness, assisted living, and fault diagnosis. To achieve latency-free monitoring, this paper presents a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> cale- and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ttention-experts based <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ulti-task neural network (SAMNet) with a large enough context of the view to make full use of the correlation between the tasks of the NILM. A shared expert learner is designed to learn a good summary of the features. Self-attention mechanism is creatively adopted to realize the weighted fusion of different experts. To address the problem of manually setting different threshold values for different appliances to decide the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on/off</i> states, we designed the network automatically labeling the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on/off</i> states. Extensive experimental results with the public datasets demonstrate the effectiveness and superiority of our proposed model.

Strategy for demand side management effectiveness assessment via a stochastic risk‐based bidding approach in a multi‐energy microgrid containing combined cooling, heat and power and photovoltaic units

This paper proposes an efficient risk-based infrastructure management approach for a multi-energy microgrid to assess the effectiveness of demand-side management (DSM) through a stochastic strategy. The studied microgrid consists of photovoltaic and combined cooling, heat and power as the energy generation units and load aggregator with the aim of DSM. Based on this structure, day-ahead operational scheduling is investigated for the microgrid in different scenarios in both summer and winter seasons. The risk consideration is also performed in the bidding procedure dealing with different scenarios and variable prices and probabilities. In this study, the conditional value at risk index is considered to assess the amount of risk and is also studied from two perspectives. In the first case, the level of risk acceptable to the investor, the load aggregator, is selected, and its impact on the day-ahead scheduling is examined. In the second case, the effect of different levels of risk-taking and their importance on the scheduling procedure are examined. The main purpose is to show the shortcomings of the traditional DSM method when load management is done for private companies with a certain level of risk and prepare the ground for future studies to solve them.

Hierarchical economic control of air-conditioning loads to compensate for fluctuations in distributed photovoltaic generation

Flexible loads such as air-conditioning loads (ACLs) can be directly controlled as demand response resources (DRRs) to provide ancillary services to the power grid. This paper proposes a hierarchical economic control strategy for the transmission system operator (TSO) to utilize ACLs to compensate for fluctuations in distributed photovoltaic (PV) generation with maximum control profit considering the uncertainties. The ACLs are aggregated by load aggregators (LAs) and controlled as two kinds of virtual generators in each LA with an additional power generation (APG) function to provide DRRs as a whole. To achieve the unification of planning and execution, an allocation algorithm and a control method for ACLs are respectively put forward to distribute the required DRRs among LAs and regulate ACLs in each LA. The proposed hierarchical economic control strategy realizes the decision-making process of purchasing DRRs through day-ahead scheduling and real-time control. In day-ahead scheduling, a flexible optimization interval division approach is presented to obtain the distribution of optimization intervals, and a stochastic optimization model is formulated to obtain the optimal purchased DRR capacity considering the PV/load forecasting errors. In real-time control, after determining the distribution of optimization intervals and the sampling points that adopt the APG function, the additional DRR capacity that needs to be purchased in real time is further optimized based on the day-ahead scheduling results. Simulation results demonstrate the effectiveness of the proposed control strategy.

Research on scheduling strategy of heterogeneous flexible load clusters with strong association consider dual-scale similarities

With the background of multi-dimensional massive load data, the resource integration of heterogeneous flexible load is conducive to fully unlock its demand response potential. For the load aggregator scheduling scenario, this paper constructs a load management framework based on distributed clustering, and proposes a heterogeneous load cluster response strategy considering dual-scale similarity and strong correlation between clusters. First, in order to extract typical daily load curve of load cluster, a k-Shape algorithm based on the similarity criterion of shape is applied, and load amplitude constraint is introduced. The algorithm transforms the problem of centroid selection into optimization problem, which overcomes the limitation of traditional clustering algorithm in curve shape measurement. Then, the FP-growth algorithm is used to mine the correlation characteristics of heterogeneous load clusters in each scheduling time window. According to the strong correlation of each cluster in response potential, the response resources are further integrated to realize the combined scheduling among clusters. Finally, based on the measured data and simulation data, the proposed method is verified by simulation. The results show that this method improves the accuracy and detail description of clustering results, and the two heterogeneous clusters in the example have strong correlation in power consumption trend.

Energy Management System for Flexible Load Aggregator Based on Redis

Energy Management System for Flexible Load Aggregator Based on Redis

A variable parameter control method for charging pile load aggregator to participate in the fast frequency market

Abstract With the access of large-scale new energy sources, fast frequency regulation resources of the power system are scarce. A variable parameter control method for charging pile load aggregator to participate in the fast frequency market is proposed in this paper. It is that charging pile load aggregator actively adjusts the active power according to the frequency of the local power system, and the response speed is within 0-2 seconds. In addition, the variable parameter control method proposed in this paper specifically controls the power of charging pile according to the speed and amplitude of frequency drop, which has strong pertinence and simple control scheme, and further improves the frequency stability of the power grid.

Optimised operation study of load aggregator based on UCCP‐IDR model

With the increasing development of integrated energy systems and the continuous promotion of integrated demand response (IDR) research, residential users with a large amount of demand response resources are drawing more and more widespread attention. In order to fully exploit the IDR potential of residential users and analyse the influence of user comfort and consumer psychology (UCCP) on IDR, the optimised operation study of load aggregator (LA) based on UCCP-IDR model is proposed. Firstly, a self-organising map algorithm for clustering is introduced, from which various types of users with differentiated consumption psychology are obtained. Secondly, user comfort and consumption psychology are taken into account to establish a user curtailable, shiftable and transformable load model. Thirdly, the user IDR optimisation model is established based on the load model considering the user response uncertainty. Finally, based on the optimisation strategy of user IDR, the total energy demand of LA is obtained, and thus the optimal operation model of LA equipment is established. The analysis shows that the proposed strategy can provide a more reliable and targeted integrated demand response strategy for residential users, and achieve a win–win situation for both LA and residential users.

Graph convolutional network-based aggregated demand response baseline load estimation

Accurate aggregated baseline load (ABL) estimation is very important for the demand response (DR) compensation settlement between system operators and DR aggregators. Current ABL estimation methods totally ignore the spatial correlation between load patterns of different customers, which will lead to large errors when the load pattern on the DR event day fluctuates largely compared with that in historical days. To this end, this paper proposes a Graph Convolutional Network (GCN)-based ABL estimation method to improve the estimation accuracy. The basic idea is to enhance the estimator's ability to capture load uncertainty by sharing load fluctuation information between different customers. The proposed method contains three main steps: First, all customers are grouped into different clusters by the K-means algorithm according to their historical typical load patterns (TLPs). Second, these clusters are transformed into an undirected graph based on an adjacency matrix reflecting the spatial correlations, which are constructed according to the difference between the TLPs in different clusters. Third, the ABL estimation is transformed into a node regression problem of the graph. Case studies on a real load dataset verify the effectiveness and superiority of the proposed method.

A framework for short-term energy consumption prediction based on room air conditioner group characteristics

Owing to the convenient installation, low energy consumption, wide applicability, and flexible operation of room air conditioner (RAC), high application potential can be achieved in terms of load dispatch or demand response by aggregating numerous single RACs’ loads. The diverse behaviors of single occupants using RACs caused by different individual demands is one of the main factors leading to deviations in short-term (e.g., hourly) energy consumption prediction. In addition, owing to the lack of suitable quantitative indicators and subjective data, previous studies are rarely focused on the group behaviors of RACs, which is more appropriate for obtaining specific regional energy consumption. Aiming at optimizing the regional power grid load fluctuations in residential building, this paper proposes a framework for predicting short-term energy consumption based on RAC group characteristics by randomly selecting approximately 2000 samples from a cloud data platform. First, this study constructs multi-dimensional group features and based on the clustering method identifies three typical groups of RACs: i) Cluster a: continuous operation in summer and high energy consumption behavior; ii) Cluster b: annual intermittent operation and high energy consumption behavior; iii) Cluster c: mainly continuous operation in summer and energy-saving behavior. Second, the study generates five different aggregation sizes of RACs and integrates them with feature selection and random forest algorithms, to predict the hourly energy consumption of a residential RAC group. High precision achievement is shown by indicator indexes, such as the coefficient of determination (R2) reaches 90% and the relative mean absolute percentage error (MAPE) is basically within 15%. Finally, by adjusting the group behavior of the load aggregator scenarios of the above three RAC groups in this method, peak shaving capacity of approximately 35% is realized on a typical summer day.

Study on the strategy of improving the life cycle economic sustainability of wind power heating projects in northern China.

The profitability of electric heating projects in Northern China is poor. A kind of combined market-oriented trading strategy to improve the economy of electric heating projects is put forward. The price game model for direct power purchase, compensation model for carbon emission reduction benefits and load aggregation revenue model are constructed, and the optimal proportion of clean energy and conventional energy consumed under the condition of ensuring heating reliability is analyzed. An electric heating project was analyzed as an example, the results showed that in the direct power purchase transaction, the power price is 0.2125 RMB/kWh, which is 0.0591 RMB/kWh lower than the catalogue tariff. Revenue from carbon trading and auxiliary service transaction is 469,913.87 RMB and 289,218.09 RMB, respectively, which is equivalent to reducing electricity cost of 0.0253 RMB/kWh and 0.0156 RMB/kWh, respectively. The ultimate power cost is 0.1716RMB/kWh, which is 0.1 RMB/kWh lower than the current catalogue tariff. Besides, the more clean energy an electric heating project consumes, the better its economy will be, and in this example, the best proportion of clean energy and conventional energy is 80% and 20%. The research results could help to promote the sustainable development of clean heating in northern China.

Trading strategy and benefit optimization of load aggregators in integrated energy systems considering integrated demand response: A hierarchical Stackelberg game

With the rise of integrated energy systems, the load aggregator's role in forming a connection between the demand and supply sides has been gaining increasing importance. Traditional demand response has gradually developed into integrated demand response. Considering consumer psychology, a bilateral integrated demand response model between the integrated energy production base, load aggregator, and users is established; the model is a hierarchical Stackelberg game. Simultaneously, a mixed integer quadratic programming-multi-verse optimizer distributed algorithm is proposed to solve the game model. During the continuous interactive gaming process, the utilities of all parties can be maximized while achieving a balance of benefits. Considering whether price incentives should be implemented and including consumers' psychological characteristics, three scenarios were established for case analysis. The results show that the proposed trading strategy can increase the benefits of the integrated energy production base, load aggregator, and users by 5%, 4%, and 23%, respectively. In addition, the penalty cost of abandoning wind and solar power and of power reserve capacity are reduced by $34.4 and $9, respectively. The introduction of the hierarchical game model ensures the sustainability of the mechanism and provides innovative ideas for the development of integrated energy systems.

Optimal dispatch of integrated energy system considering integrated demand response resource trading

In order to reduce the impact of renewable energy output and load fluctuations and improve the flexibility of the integrated energy system (IES), it is necessary to further promote user participation in integrated demand response (IDR). Therefore, this paper constructs a hierarchical framework that enables the transaction of IDR resources among users by combining the information interaction network established by blockchain and the energy management network established by energy management system. This paper also analyses the comfort of users, the cost of energy purchase by users and the cost of energy use by load aggregators, and then develops a two-layer optimization model. The results of the simulation show that the model and trading framework constructed in this paper can realize the trading of IDR resources among users, which effectively promotes the participation of users in IDR, reduces the cost of users and load aggregators, reduces the loss of IDR resources, enables more effective integration of dispersed IDR resources and improves the flexibility of IES.

Ground Source Heat Pump Modeling, Operation, and Participation in Electricity Markets

Over the last decades, Ground Source Heat Pump (GSHP) systems have grown in popularity and acceptance worldwide, having the potential to bring about significant benefits, especially if these systems participate in electricity markets through a load aggregator to optimize their operations and provide services such as load shifting and demand reduction, as proposed here. In this context, GSHP systems can be considered as an economically viable and attractive alternative to existing Heating Ventilation and Air Conditioning (HVAC) systems for space heating/cooling in buildings and houses from an aggregator point of view. Hence, this paper presents a detailed mathematical model for a GSHP with a vertical U-pipe Ground Heat eXchanger (GHX) configuration to provide residential space heating/cooling, integrating several such GSHPs into a load aggregator model. Then, a two-stage operational strategy for the GSHP pricetaker aggregator participating in day-ahead and real-time electricity markets is proposed, to determine the optimal annual heating/cooling load dispatch to control the temperatures for a community of houses that minimizes the aggregator’s cost. Detailed numerical studies and comparisons with HVAC systems are carried out to demonstrate the feasibility and benefits of the proposed aggregated operation of GSHPs in electricity markets.

Optimal Demand Response Incorporating Distribution LMP With PV Generation Uncertainty

The utilization of aggregated demand-side flexibility via demand response (DR) has become a promising pathway for the integration of renewable energy resources in power systems. Nowadays, there are several management strategies for DR such as the price-based transactive control strategies. However, many of such existing price-based control strategies neglect the physics and operational constraints of the underlying distribution networks when computing the price, raising concerns regarding their theoretical and practical values. This paper studies this issue and investigates optimal DR (ODR) by incorporating the distribution locational marginal price (DLMP). In particular, we discuss DR in connection with DLMPs and propose a multi-period bilevel optimization problem to find the ODR strategy. The objective is to minimize the peak load, load fluctuation, and payments of load aggregators. In addition, a robust bilevel ODR model is formulated to provide a robust ODR strategy while minimizing operating costs under the worst-case realization of uncertainties; this mitigates the impact of forecasting errors on renewable energy resources. Then, we propose an efficient solution approach by employing the Karush-Kuhn-Tucker conditions and strong duality. Simulation results are presented to illustrate the mutual impacts of the interaction between DR and DLMP and the benefits of the robust ODR strategy.

Effect of Routing Flexibility on the Performance of Order Release Policies in a Flexible Job Shop with Sequence-Dependent Setup Time: A Simulation Study

ABSTRACT Order review and release (ORR) in a flexible job shop considering sequence-dependent setup time (SDST) is a complicated job shop problem. Routing flexibility helps in increasing the flexibility of the system by providing alternative routes to jobs. This research work assesses the effect of routing flexibility on order release policies in a flexible job shop considering SDST within a stochastic and dynamic (SCDM) manufacturing environment. In this study, five order release policies, viz., Corrected Aggregate Load Approach (CALA), Aggregate Workload Trigger (AGGWLT), Corrected Workload Trigger (CorrWLT), Work Center Workload Trigger (WCWLT), and Lancaster University Management School Corrected Order Release (LUMSCOR), are considered with planned release date sequencing rule. Four performance measures are considered in the present research work, i.e., mean throughput time, mean lead time, mean tardiness, and total setups. For experimental purposes, a simulation model is developed with six routing flexibility levels with the help of Promodel® software. Results indicate that the performance of all ORR policies can be improved by considering routing flexibility along with it. For a given ORR policy, as routing flexibility increases, there is a decrease in system performance measures up to a certain level, and after that, it starts increasing. Thus, routing flexibility has an optimum level. The least value of workload trigger level for all ORR policies provides the best results for mean throughput time performance measure. Further, as the workload trigger level increases, the best results are obtained for the other three performance measures except the mean throughput time.

A data-driven load forecasting method for incentive demand response

The participation of incentive demand response (IDR) can improve power grid flexibility, and reduce peak shaving pressure. However, the further development of incentive demand response services will be limited by the uncertainty of user response behavior. To tackle this problem, this paper proposes a data-driven load forecasting method for IDR, which considers consumer behavior. Firstly, we describe the power market auxiliary service operation mechanism for load aggregators, through which the ability of demand-side resources to respond to auxiliary services is improved. Then, add an attention layer to improve the traditional long and short memory (LSTM) model, and propose an IDR load forecasting method based on this model, which considers the model’s learning of historical data of user behavior. Finally, establish a simulation model to verify the effectiveness of the improved LSTM model forecasting. The proposed method is simulated and compared with the traditional LSTM method and k-nearest neighbor prediction method. The results show that compared with the other two methods, the mean error, root means square error, and mean absolute percentage error in the improved LSTM method are reduced by 7.31 MW, 8.32 MW, 2.06% and 10.62 MW, 13.46 MW and 3.09%, respectively, which effectively improves the accuracy of forecasting and increase the participation of demand-side resources in the power auxiliary service market.

Research on flexible smart home appliance load participating in demand side response based on power direct control technology

More and more flexible smart home appliance loads are participating in the demand-side response of the power system, which leads the complexity of power system load regulation is gradually increasing. In order to accurately regulate and control the flexible smart home appliance loads, this paper constructs a regulation architecture according to the load control method which is based on the load aggregator. And then, combined with the regulation architecture, this paper further establishes the value indicator of flexible smart home appliance loads. By analyzing the air-conditioner load proportion of smart homes on basis of the load value indicators, this paper effectively calculates the adjustable potential of the single fixed frequency air-conditioner and inverter air-conditioner load. Finally, through the simulation study, the demand response capability ΔPt and continuously controlled time Δtlast of air-conditioners are accurately calculated. Meanwhile, the adjustable potential of the air-conditioner load in smart home during the demand response period is obtained.