Aggregate Load Research Articles

Renewable energy time series regulation strategy considering grid flexible load and N-1 faults

As the proportion of renewable energy in electricity generation continues to increase, the uncertainty of renewable energy has brought significant challenges to power system security. Traditional renewable energy dispatching methods have less research on power system failure risk analysis due to economic considerations. In this paper, a renewable energy time series regulation strategy considering N-1 faults is proposed to improve the power system economy and security. In this strategy, by researching flexible load aggregation models, the time-series regulation model of aggregation loads is determined. Then the network N-1 security analysis divided into generator detection and branch detection is conducted. Determining possible N-1 faults of the system, the scheme minimizes the operating cost and grid risk. Adopting the methods above, the most desired renewable energy time-series regulation results are obtained. The results have shown that after adopting the proposed strategy in the improved system, the solar and wind curtailment volume is reduced by 38.10%, and the operating cost is reduced by 24.60%. In a daily cycle, the maximum power of all branch power overruns at the most risky moment is reduced by 20.36%, and the sum of the total power of all branch power overruns is reduced by 40.30%.

Short-term multivariate time series load data forecasting at low-voltage level using optimised deep-ensemble learning-based models

Increasing the renewable energy penetration, especially photovoltaic systems, requires accurate and short-term load forecasting for every individual electricity customer. This can significantly help distributed network service providers (DNSP) plan and operate electrical networks and provide better quality and more reliable electricity services to their customers. Due to the strong volatility of the load data, an optimised deep-ensemble learning methodology is proposed to develop several deep learning models to forecast the load data in individual and aggregate load scenarios. The adaptive wind-driven optimisation (AWDO) algorithm is used to tune the hyperparameters of four of the developed deep learning models, significantly improving their performance. Tuning the deep learning models’ hyperparameters shows significant improvements in the accuracy, as well as training, and testing times. In addition, a hybrid ensemble strategy that contains bagging, random Subspace, and boosting (BRSB) with ensemble pruning is adapted in the optimised deep learning-based models to extract deep features from multivariate data. In this context, the bidirectional long-short-term memory optimised by the AWDO algorithm (Bi-LSTM-AWDO) based hybrid ensemble learning forecasting model is compared exhaustively with several benchmark models, including the recently developed models in the state-of-the-art. The average root mean square error (RMSE), and the average mean absolute percentage error (MAPE) of the Bi-LSTM-AWDO model for individual loads are 0.121 kW, and 7.55%, respectively, while they are 0.025 kW, and 1.51% for aggregate loads, respectively. Accordingly, the Bi-LSTM-AWDO model outperforms other models in forecasting short-term load data for individual and aggregate households using actual smart meters’ measurements.

Meso-structural evaluation of asphalt mixture skeleton contact based on Voronoi diagram

The Voronoi diagram is a fundamental data structure for spatial neighborhood relations used in various fields related to geometric information. Taking four asphalt mixtures as the research object, this study established the skeleton contact Voronoi diagram model to characterize the contact meso-structure between coarse aggregates, and extracted the quantitative information of skeleton contact space. The results show that the skeleton contact Voronoi diagram model can represent important spatial information of asphalt mixtures from the meso-structure including the homogeneity of asphalt mixtures, the extrusion relationship between coarse and fine aggregates, and the skeleton contact distribution characteristics, etc. The shape of the connected tree reflects the coarse aggregate contact distribution and load transfer trend, and the order of the connected tree reflects the contact connectivity strength of the skeleton. We used Voronoi diagram area Vs, connected tree area TVs and the total length of contact points L to quantitatively evaluate the skeleton contact of asphalt mixtures. In the design of asphalt mixtures with dense skeletons, the evaluation criteria of skeleton contact characteristics include Vs (30–180 mm2) proportion ≥75 %, Tr-1 and Tr-2 proportion ≤75 % and L ≥70. The results provide new ideas to promote the quality of asphalt mixtures and pavement construction.

A flexible load aggregation framework for optimal distribution system operation

A flexible load aggregation framework for optimal distribution system operation

Economic dispatch of generation load aggregators based on two-stage robust optimization

Introduction: In recent years, with the rapid development of renewable energy generation, the stability of the power grid has been greatly reduced. In response to this problem, integrating the user side transferable load into the power market has become the key to the development of future power grid. At present, large transferable loads have entered the electricity market in some pilot areas of China, but the relevant research on small and medium-sized transferable users entering the electricity market is still few.Methods: This paper proposes the concept of generation load aggregators. A two-stage generation load aggregator robust optimization model is developed to obtain the scheduling scheme with the lowest operating cost under the worst scenario. The model consists of distributed renewable power, transferable load, self-provided power, energy storage, etc. Uncertainties of renewable energy and load are introduced in the model. By using the column constraint generation algorithm and strong pairwise theory, the original problem is decomposed into the main problem and sub-problems to be solved alternately, so as to obtain the scheduling scheme with the lowest operating cost in the worst scenario under different conservatism.Results: The solved results are compared with those without the generation load aggregator, illustrating the role of the generation load aggregator in relieving peak and valley pressure on the grid from the load side, reducing the cost of electricity for loads, and promoting the consumption of renewable energy. The comparison with the deterministic optimization algorithm shows a significant decrease in the total cost and validates the performance of the selected solution algorithm. The boundary conditions for the use of energy storage by generation load aggregators for peak and valley reduction under the time-sharing tariff mechanism are also derived.Discussion: This study can provide reference for the investors of generation load aggregators when planning whether to install energy storage or the scale of energy storage, and also help the power market management department to design a reasonable incentive mechanism.

Integrating Electric Vehicles to Power Grids: A Review on Modeling, Regulation, and Market Operation

Fossil energy consumption and environmental protection issues have pushed electric vehicles (EVs) to become one of the alternatives to traditional fossil-fuel vehicles. EV refers to a vehicle that uses electric energy as power and is driven by an electric motor. The electric energy of EVs is stored in batteries. When the EV is not traveling, the battery can provide power for other loads. Therefore, with the increase in the number of EVs and the load of the power grid, the EV-to-grid (V2G) mode, which uses EVs to supply power to the power grid, has gradually entered the field of vision of researchers. The physical connection mode, charge and discharge technology, and energy management strategy are the main topics of the current review papers; however, there is a lack of systematic research on V2G modeling, framework, and business models. This paper describes the concepts of the spatio-temporal distribution model and the adjustable capacity of EVs. In addition, common constraints and methods in optimization are introduced. Moreover, this paper introduces the interactive relationship among power grids, load aggregators, and EV users. Furthermore, the business model of V2G is introduced and analyzed from various perspectives. Finally, the future development of V2G is pointed out. This paper’s goal is to provide an overview of the present V2G application scenarios and to identify any challenges that must be overcome.

InVivo Cerebral Imaging of Mutant Huntingtin Aggregates Using 11C-CHDI-180R PET in a Nonhuman Primate Model of Huntington Disease.

Huntington disease (HD) is a neurodegenerative disorder caused by an expanded polyglutamine (CAG) trinucleotide expansion in the huntingtin (HTT) gene that encodes the mutant huntingtin protein (mHTT). Visualization and quantification of cerebral mHTT will provide a proxy for target engagement and a means to evaluate therapeutic interventions aimed at lowering mHTT in the brain. Here, we validated the novel radioligand 11C-labeled 6-(5-((5-methoxypyridin-2-yl)methoxy)benzo[d]oxazol-2-yl)-2-methylpyridazin-3(2H)-one (11C-CHDI-180R) using PET imaging to quantify cerebral mHTT aggregates in a macaque model of HD. Methods: Rhesus macaques received MRI-guided intrastriatal delivery of a mixture of AAV2 and AAV2.retro viral vectors expressing an HTT fragment bearing 85 CAG repeats (85Q, n = 5), a control HTT fragment bearing 10 CAG repeats (10Q, n = 4), or vector diluent only (phosphate-buffered saline, n = 5). Thirty months after surgery, 90-min dynamic PET/CT imaging was used to investigate 11C-CHDI-180R brain kinetics, along with serial blood sampling to measure input function and stability of the radioligand. The total volume of distribution was calculated using a 2-tissue-compartment model as well as Logan graphical analysis for regional quantification. Immunostaining for mHTT was performed to corroborate the invivo findings. Results: 11C-CHDI-180R displayed good metabolic stability (51.4% ± 4.0% parent in plasma at 60 min after injection). Regional time-activity curves displayed rapid uptake and reversible binding, which were described by a 2-tissue-compartment model. Logan graphical analysis was associated with the 2-tissue-compartment model (r 2 = 0.96, P < 0.0001) and used to generate parametric volume of distribution maps. Compared with controls, animals administered the 85Q fragment exhibited significantly increased 11C-CHDI-180R binding in several cortical and subcortical brain regions (group effect, P < 0.0001). No difference in 11C-CHDI-180R binding was observed between buffer and 10Q animals. The presence of mHTT aggregates in the 85Q animals was confirmed histologically. Conclusion: We validated 11C-CHDI-180R as a radioligand to visualize and quantify mHTT aggregated species in a HD macaque model. These findings corroborate our previous work in rodent HD models and show that 11C-CHDI-180R is a promising tool to assess the mHTT aggregate load and the efficacy of therapeutic strategies.

Service-quality based pricing approach for charging electric vehicles in smart energy communities

Guiding electric vehicles (EVs) in smart energy communities to participate in demand response can significantly benefit users and the load aggregator. However, when the user's travel plans are uncertain, the EV may end up charging before the planned time, leaving the user with an insufficient power supply. This can occur because the control program chooses to charge the EV during periods of low electricity prices to reduce user costs. To address this issue, a new strategy for charging pricing based on service-quality is proposed to help users deal with the uncertainty of travel plans and promote user participation in demand response. In this paper, the proposed charging pricing of EVs consists of three parts. Firstly, the model of charging packages with different service qualities is designed for users. The differences in charging packages are reflected in the percentage of power supply and price within each time slot. Charging packages provide users with at least a certain amount of power in each time slot, allowing them to better cope with the uncertainty of their travel plans. Secondly, a subscription model for charging packages is built to match the user's travel plans. Finally, the pricing model of the charging package is established, representing the master-slave game between the load aggregator and the user. The model is linearized by the KKT condition. The results show that the proposed strategy helps users cope with the uncertainty of their travel plans, reducing charging cost for users by 16.39% while increasing revenue for the load aggregator by 14.8%.

Distributed demand response charging control of multiple plug‐in electric vehicle clusters

AbstractThis paper is devoted to the distributed demand response (DR) charging control of plug‐in electric vehicles (PEVs) for frequency regulation in power systems in terms of load following service. Specifically, PEVs are divided into different clusters according to their parking place under different energy management systems. Each EV cluster is modelled by a transport‐based load aggregate model with the input being the charging rate, and the output is the aggregate power. Based on the aggregate charging control model, a novel dynamic real‐time distributed pinning control algorithm is proposed to coordinate the charging rates such that the aggregate charging power of PEVs can follow a given reference power trajectory. The theoretical analysis shows that if the reference power profile is in the trackable area of all PEVs' charging power and the ramping rate is restrained by a predefined bounded constraint, then the demand response charging tracking control is solvable. Finally, simulation results on an EV system with twelve PEV clusters are presented to show the effectiveness of the proposed demand response control algorithm.

Optimal dispatching of electric‐heat‐hydrogen integrated energy system based on Stackelberg game

AbstractThe interest conflict among entities in the integrated energy system (IES) has a great challenge to operation decisions of IES. With regards to this, an optimal dispatching model of electric‐heat‐hydrogen IES based on Stackelberg game is proposed. First, an energy producer (EP) model is formulated which considered the full utilization of hydrogen energy and involved the conversion of hydrogen energy to electricity and heat energy. Meanwhile, the demand response amount is integrated into the objective function of load aggregator (LA) in order to encourage consumers to adjust their consumption behaviour. Second, by analyzing the characteristics of price information interaction among EP, energy system operator (ESO), and LA, the payoffs of each entity in IES are reformulated. Finally, a Stackelberg game model is established with ESO as the dominator guiding price information, EP and LA as the followers whose private information is confidential. Genetic algorithm and quadratic programming algorithm are employed to solve the developed model. Numerical experiments are carried out on an actual park‐level IES in northern China to demonstrate the effectiveness of the proposed model in promoting the benefit equilibrium among various entities.

Research on the bi-layer low carbon optimization strategy of integrated energy system based on Stackelberg master slave game

With increasing reforms related to integrated energy systems (IESs), each energy subsystem, as a participant based on bounded rationality, significantly influences the optimal scheduling of the entire IES through mutual learning and imitation. A reasonable multiagent joint operation strategy can help this system meet its low-carbon objectives. This paper proposes a bilayer low-carbon optimal operational strategy for an IES based on the Stackelberg master-slave game and multiagent joint operation. The studied IES includes cogeneration, power-to-gas, and carbon capture systems. Based on the Stackelberg master-slave game theory, sellers are used as leaders in the upper layer to set the prices of electricity and heat, while energy producers, energy storage providers, and load aggregators are used as followers in the lower layer to adjust the operational strategy of the system. An IES bilayer optimization model based on the Stackelberg master-slave game was developed. Finally, the Karush-Kuhn-Tucker (KKT) condition and linear relaxation technology are used to convert the bilayer game model to a single layer. CPLEX, which is a mathematical program solver, is used to solve the equilibrium problem and the carbon emission trading cost of the system when the benefits of each subject reach maximum and to analyze the impact of different carbon emission trading prices and growth rates on the operational strategy of the system. As an experimental demonstration, we simulated an IES coupled with an IEEE 39-node electrical grid system, a six-node heat network system, and a six-node gas network system. The simulation results confirm the effectiveness and feasibility of the proposed model.

Dynamic modeling and uncertainty quantification of district heating systems considering renewable energy access

The district heating system (DHS) is one of the essential carriers for coordinating renewable energy with fossil energy and achieving flexible energy consumption. Considering the uncertainty of both the renewable energy units' power and the consumers' aggregate loads impact the transport process of the DHS, the thermal characteristics of the DHS transportation and the node temperature response under the source-load uncertainty need to be quantified and analyzed. This paper proposed a method to calculate the nodes' temperature dynamic response of the DHS under multiple source-load uncertainty scenarios. The prediction and combination methods of supply and demand probability intervals of source and load nodes under varying credibility levels were investigated. Additionally, the effects of source-load uncertainty boundary conditions on the DHS transport process were analyzed. The temperature response of the nodes was calculated based on the established dynamic DHS model under multiple source-load uncertainty scenarios. A DHS in Beijing was selected for case validation and analysis, and it has 90 nodes and 109 pipes. The simulation results showed that the relative average error of node flow rate in the dynamic DHS model was 3.02%, and the average difference in node temperature was 1 °C. The probability distribution semi-analytical method and the Bayesian credible interval calculation method can accurately describe the uncertainty on both source and load sides. The models and algorithms proposed in this paper could effectively quantify the nodes' dynamic temperature response under the source-load uncertainty.

Optimal configuration method of electric vehicle’s participating in Load Aggregator’s VPP low-carbon economy

This paper aims to improve the reliability of multi-type load power supply, reduce the disorder of electric vehicle (EV) charging, and ensure the low-carbon economic operation of virtual power plant (VPP). Therefore, the optimal allocation method of low-carbon economy for VPP involving load aggregator (LA) participated by EV is proposed. First, a probabilistic model of electric load demand of a VPP is constructed, and an aggregation model of multiple types of uncertain loads is established based on the probabilistic model. Secondly, the disorder of EV charging is analyzed. Considering the time-of-use price policy, an optimization model with the maximum profit of load aggregator is established. Finally, the supply and demand matching model of VPP is established, and the minimum carbon emission and maximum net income of virtual power plant are optimized. Through simulation analysis, it is verified that this optimal configuration method can effectively improve the economics of EVLA and VPP, and ensure low-carbon operation of VPP.

Federated learning application in distributed energy trading in integrated energy system

Privacy protection in electricity market transactions is a long-term topic, and it must be considered in the application of any new power system project today. This paper explores a new welfare optimization method considering privacy protection in the field of energy trading based on federated learning (FL). First, this paper models an integrated energy system (IES) with one distributed network manager (DNM) and multiple load aggregators (LAs). This model adopts the idea of Stackelberg game, makes DNM dominate the trading game, and makes LAs as followers. Then, it uses the FL-Stackelberg method proposed for the first time to solve the established individual welfare optimization model participating in the game and compares the results with those of the traditional hierarchical optimization method and the Mathematical Program with Equilibrium Constraint optimization method. Numerical results at the end of the paper prove the good accuracy and calculation speed of the proposed FL-Stackelberg​ optimization algorithm, and demonstrate the reliability of FL in engineering practice of IES.

Stackelberg equilibrium-based energy management strategy for regional integrated electricity–hydrogen market

This paper develops an optimal energy bidding mechanism for the regional integrated electricity–hydrogen system (RIEHS) considering complex electricity–hydrogen energy flow and further presents an electricity–hydrogen optimization management strategy based on Stackelberg game. The transaction mode of the RIEHS is first introduced, and the optimization models for the three market game participants are established. Then, the Stackelberg game-based bidding mechanism is formulated, where the electricity–hydrogen operator (EHO) is the leader and the regional electricity–hydrogen prosumer (REHP) and load aggregator (LA) are the followers. The EHO dominates the game through energy bidding, and REHP and LA respond to the bidding decision. The Stackelberg equilibrium of the formulation is obtained by applying the differential evolutionary algorithm combined with quadratic programming (DEA-QP). Finally, a demonstration case is studied to analyze the market behavior of the three market players and further validate the effectiveness of the proposed strategy. The proposed strategy is able to produce additional economic benefits to REHP and LA and improve the utilization of hydrogen.

Probability-based temperature-set-point control of aggregate air-conditioning loads

The Air-Conditioners (ACs) are important participants in the Demand Response (DR) programs due to their good heat/cold storage characteristics. However, traditional temperature-set-point control methods of ACs neglect the fact that Minimum Step-size of Temperature Adjustment (MSoTA) for most ACs is no smaller than 1 °C. In this paper, the load following control method is designed for ACs with MSoTA of 1 °C. To realize smooth control of the aggregate ACs, probability-based temperature-set-point broadcasting control method is designed. By adopting a prioritized mechanism, the room temperature deviation is lowered. The testing examples verify the effectiveness of the proposed method in bi-directional, smooth, and comfortable regulation of the aggregate ACs.

Hierarchical learning, forecasting coherent spatio-temporal individual and aggregated building loads

Optimal decision-making compels us to anticipate the future at different horizons. However, in many domains connecting together predictions from multiple time horizons and abstractions levels across their organization becomes all the more important, else decision-makers would be planning using separate and possibly conflicting views of the future. To this end, this work proposes a novel multi-dimensional hierarchical forecasting method built upon structurally-informed machine-learning regressors. A generic formulation of multi-dimensional hierarchies, reconciling spatial and temporal dimensions under a common frame is initially defined. Next, a coherency-informed hierarchical learner is developed built upon a custom loss function leveraging optimal reconciliation methods. The coherency of the produced hierarchical forecasts is then secured using similar reconciliation techniques, granting decision-makers a common view of the future serving aligned decision-making. The method is evaluated on two different case studies to predict building electrical loads across spatial, temporal, and spatio-temporal hierarchies. Although the regressor natively profits from computationally efficient learning, results displayed disparate performances, demonstrating the value of hierarchical-coherent learning in only one setting. Yet, existing obstacles were clearly delineated, presenting distinct pathways for future work. Overall, the paper expands and unites traditionally disjointed hierarchical forecasting methods providing a fertile route toward a novel generation of forecasting regressors.

Application of Demand-Side Management (DSM) for evaluation and optimization of Electric Vehicle’s charging cycles

Electric vehicles (EVs) have been known as potential transportation alternatives for internal combustion engines thanks to lower carbon emissions and high energy efficiency. The combination of EVs with other renewable energy sources (RES) is a viable solution for eliminating the emission on the road and during the electricity generation process, reducing the reliance of EVs energy demand on the non-renewables. However, the higher penetration of EVs and unpredictable charging dynamics could burden the power system tremendously. This study proposes a demand-side management (DSM) strategy for an EVs-integrated residential grid with the assistance of grid-tied rooftop photovoltaic (PV) systems. The main objectives are to minimize the peak and fluctuation load while ensuring the EVs’ effective charging and vehicle-to-grid (V2G) scheme based on time-of-use (TOU) tariff and real-time pricing (RTP). The EVs’ traveling distances, driving periods, initial state-of-charge (SOCs), and plugging states are considered. One-day simulation in different scenarios was carried out to evaluate the strategies. The results indicate that the proposed model can reduce load demand, flatten the load curve, and reduce electricity costs, maintaining the system’s stability. The approach can be an indicator for load aggregators and utilities to manage PEVs as DSM tools in the system.

Unlock the Thermal Flexibility in Integrated Energy Systems: A Robust Nodal Pricing Approach for Thermal Loads

The thermal flexibility of buildings in integrated energy systems (IES) has great potential to improve the operational economy and wind power consumption. To incentivize the thermal flexibility of buildings under uncertainties, we propose a robust nodal pricing (RNP) model for thermal loads based on the Stackelberg game approach. The RNP model adopts a bilevel framework in which the IES operator plays the leader at the upper level while the thermal load aggregators (TLA) play the followers at the lower level. The IES operator problem optimizes the heat prices and dispatch plan, which is modeled as a two-stage robust optimization problem to address the uncertainties in the renewables and the loads. The TLA problem optimizes the thermal loads of buildings to minimize the energy cost and thermal comfort loss, which is modeled as a distributionally robust chance-constrained optimization problem to address the uncertainty in outdoor temperature. Then, we convert the TLA model into deterministic quadratic programming and prove that Slaters condition holds under a mild assumption. Using it, the TLA model is equivalently converted into Karush-Kuhn-Tucker conditions, leading to a reformulation of a classical two-stage robust optimization model for the RNP model. Case studies compare different pricing methods and verify the superiority of the proposed method.

Low-carbon optimal operation strategy of multi-park integrated energy system considering multi-energy sharing trading mechanism and asymmetric Nash bargaining

In the current energy transactions of multi-park integrated energy system (M-PIES), aiming at the problems that the interaction between the supply side and the demand side is not fully considered, the sharing potential of multiple energy sources is not fully tapped, the benefit distribution mechanism considering the contribution of the load aggregator (LA) is lacking, and with the all-round development of the carbon trading market (CTM), the existing ladder-type carbon trading (LCT) mechanism has insufficient capacity to limit CO2 discharges of M-PIES, etc., this paper puts forward a low-carbon optimal operation strategy of M-PIES considering the multi-energy sharing trading mechanism (MSTM) and asymmetric Nash bargaining (ANB). Firstly, the interaction between supply and demand (IS&D) is fully considered, and the benefit maximization model of PIES operator-LA multi-agent alliance considering MSTM and improved ladder-type carbon trading mechanism (ILCTM) is established by using Nash negotiation theory (NNT). Then, according to the different bargaining capabilities of each agent, a profit distribution model based on ANB is proposed, and it is solved by alternating direction method of multipliers (ADMM). Finally, through the comparison of different cases, it is proved that the proposed method can further reduce CO2 discharges of M-PIES, improve the benefits of each agent and alliance, and realize the fair and reasonable distribution of energy sharing (ES) benefits.