Abstract:This paper establishes a two-layer data-driven robust scheduling method to deal with the significant computational complexity and uncertainties in scheduling industrial heat loads. First, a two-layer deterministic scheduling model is proposed to address the computational burden of utilizing flexibility from a large number of bitumen tanks (BTs). The key feature of this model is the capability to reduce the number of control variables through analyzing and modeling the clustered temperature transfer of BTs. Second, to tackle the uncertainties in the scheduling problem, historical data regarding BTs are collected and analyzed, and a data-driven piecewise linear Kernel-based support vector clustering technique is employed to construct the uncertainty set with convex boundaries and adjustable conservatism, based on which robust optimization can be conducted. The case results indicate that the proposed method enables the utilization of flexibility in BTs, improving the level of onsite photovoltaic consumption and reducing the aggregated load fluctuation.

Abstract:Photovoltaic (PV) inverter, as a promising voltage/var control (VVC) resource, can supply flexible reactive power to reduce microgrid power loss and regulate bus voltage. Meanwhile, active power plays a significant role in microgrid voltage profile. Price-based demand response (PBDR) can shift load demand via determining time-varying prices, which can be regarded as an effective means for active power shifting. However, due to the different characteristics, PBDR and inverter-based VVC lack systematic coordination. Thus, this paper proposes a PBDR-supported three-stage hierarchically coordinated voltage control method, including day-ahead PBDR price scheduling, hour-ahead reactive power dispatch of PV inverters, and real-time local droop control of PV inverters. Considering their mutual influence, a stochastic optimization method is utilized to centrally or hierarchically coordinate adjacent two stages. To solve the bilinear constraints of droop control function, the problem is reformulated into a second-order cone programming relaxation model. Then, the concave constraints are convexified, forming a penalty convex-concave model for feasible solution recovery. Lastly, a convex-concave procedure-based solution algorithm is proposed to iteratively solve the penalty model. The proposed method is tested on 33-bus and IEEE 123-bus distribution networks and compared with other methods. The results verify the high efficiency of the proposed method to achieve power loss reduction and voltage regulation.

Abstract:The spot flexibility markets are before the real-time energy exchange, allowing demand-side management to reduce energy consumption during peak periods. In these markets, demand aggregators must quickly choose the customers ’reduction bids that fulfill grid requirements. This clearing procedure is challenging due to the computational complexity of selecting the optimal bids. Therefore, developing a clearing mechanism that avoids searching the entire flexibility bid space while respecting grid constraints is essential for the smooth operation of the spot flexibility market. This paper presents a clearing mechanism with reduced computational complexity of the winner determination problem in spot flexibility market for demand aggregators carrying out reductions in energy consumption. The proposed approach transforms customers’flexibility bids into a reward-based function. Afterward, the gradient-based optimization solves the bid selection problem. This approach helps demand aggregators achieve satisfactory energy reductions within an appropriate delay for spot flexibility markets. A comparative study presents the effectiveness of the proposed approach against commonly used approaches: hybrid particle swarm optimization genetic algorithm and combinatorial search.

Abstract:The virtual power plant (VPP) facilitates the coordinated optimization of diverse forms of electrical energy through the aggregation and control of distributed energy resources (DERs), offering as a potential resource for frequency regulation to enhance the power system flexibility. To fully exploit the flexibility of DER and enhance the revenue of VPP, this paper proposes a multi-temporal optimization strategy of VPP in the energy-frequency regulation (EFR) market under the uncertainties of wind power (WP), photovoltaic (PV), and market price. Firstly, all schedulable electric vehicles (EVs) are aggregated into an electric vehicle cluster (EVC), and the schedulable domain evaluation model of EVC is established. A day-ahead energy bidding model based on Stackelberg game is also established for VPP and EVC. Secondly, on this basis, the multi-temporal optimization model of VPP in the EFR market is proposed. To manage risks stemming from the uncertainties of WP, PV, and market price, the concept of conditional value at risk (CVaR) is integrated into the strategy, effectively balancing the bidding benefits and associated risks. Finally, the results based on operational data from a provincial electricity market demonstrate that the proposed strategy enhances comprehensive revenue by providing frequency regulation services and encouraging EV response scheduling.

Abstract:As the penetration of intermittent renewable energy resources in microgrids (MGs) continues to grow globally, optimal operation management becomes increasingly crucial due to the variability of these sources. One potential solution to this challenge is the use of demand response (DR) programs, which are practical and relatively low-cost options. However, ensuring the security of MG operation also requires evaluating its flexibility by determining the acceptable boundaries of uncertain variables. Additionally, in real-world operational decision-making problems, there is a simultaneous optimization of multiple objectives, including the maximization of system flexibility and the minimization of system cost. This paper presents a methodology for developing a cost-aware flexibility evaluation method for MGs connected to the upstream grid, which are subject to volatile market prices. The model is based on the feasibility analysis of the uncertain space of wind power generation and load, and it also investigates the level of inflexibility present in the system. The impact of the DR program on the flexibility of MGs is quantified through a case study. The case study confirms the success of the proposed method and underscores the significance of cost modeling in flexibility evaluation problems.

Abstract:Thermostatically controlled loads (TCLs) on the demand side have been a vital energy resource in smart grids. To efficiently utilize the large-scale TCLs and enhance the flexibility of micro-community systems, this paper proposes a distributed coordinated control strategy based on the distributed model predictive control (MPC). To achieve the adaptive coordinated control among TCLs and consider user comfort constraints, a distributed dual-layer internal control strategy based on MPC is established on a scalable communication network. This strategy achieves the efficient utilization of TCLs in a distributed manner and notably improves the convergence speed through sparse network communication between neighbors. For external resource utilization of TCLs, a multi-timescale scheduling framework is proposed to realize the pre-allocation of electricity. Furthermore, the feasibility of the proposed distributed coordinated control strategy is confirmed through comparative case analysis.

Abstract:The increasing penetration of wind power poses challenges to the power grid operation and scheduling. Yet, if the uncertainty of wind power can be economically and effectively managed on the source side, it can drive the power grids towards renewable-dominant future. In this paper, an enhanced scheduling strategy for wind farm-flexible load joint operation system (WF-FLJOS) is proposed. The proposed strategy is designed to manage the uncertainty of wind power on the generation side when integrated into a large-scale power grid. Moreover, it can contribute to saving energy costs on the load side. Compared with the current wind farm operation rules, more stringent assessment requirements are put forward for wind power output accuracy, and the internal organization framework of WF-FLJOS is designed. For potential power violations of wind farms and flexible loads, the violation penalty mechanisms are developed to regulate the behavior of the participants. The joint operation model of the WF-FLJOS is proposed and the submission and tracking approach of the generation schedule for the wind farm is investigated. Numerical results indicate that the proposed strategy can not only improve the ability of the wind farm to track the generation schedule, but also consider the benefits of both the farm side and the load side. Meanwhile, the proposed strategy effectively reduces the schedule adjustment pressure on the main grid caused by the rolling correction mode of the intraday schedule for wind farms.

Abstract:Public buildings present substantial demand response (DR) potential, which can participate in the power system operation. However, most public buildings exhibit a high degree of uncertainties due to incomplete information, varying thermal parameters, and stochastic user behaviors, which hinders incorporating the public buildings into power system operation. To address the problem, this paper proposes an interval DR potential evaluation method and a risk dispatch model to integrate public buildings with uncertainties into power system operation. Firstly, the DR evaluation is developed based on the equivalent thermal parameter (ETP) model, actual outdoor temperature data, and air conditioning (AC) consumption data. To quantify the uncertainties of public buildings, the interval evaluation is given employing the linear regression method considering the confidence bound. Utilizing the evaluation results, the risk dispatch model is proposed to allocate public building reserve based on the chance constrained programming (CCP). Finally, the proposed risk dispatch model is reformulated to a mixed-integer second-order cone programming (MISOCP) for its solution. The proposed evaluation method and the risk dispatch model are validated based on the modified IEEE 39-bus system and actual building data obtained from a southern city in China.

Abstract:Demand response (DR) is a practical solution to overcoming the challenges posed by the volatility and intermittency of the renewable generation in power systems. Industrial electricity demand is growing rapidly, which makes the DR potential estimation of industrial user critical for the DR implementation. In this paper, a unified model for estimating DR potential in the production processes of aluminum, cement, and steel is proposed on the basis of their unique operational characteristics. Firstly, considering the typical characteristic constraints of different industrial users, a DR potential estimation model is developed to capture typical industrial user response behavior under various operational and economic factors. The proposed estimation model is further refined to account for the uncertain and subjective factors present in the actual estimation environment. Secondly, a virtual data acquisition method is introduced to obtain the private virtual parameters required in the estimation process. Then, an industrial user participation threshold is presented to determine whether industrial users may participate in DR at a given time with consideration of their response characteristics. The industrial users may not always act with perfect rationality, and the response environment remains uncertain. In addition, the subjective factor in this paper includes the proposed threshold and the bounded rationality. Finally, an improved DR potential estimation model is proposed to reduce the difficulties in the actual estimation process. The simulation results validate the effectiveness of the proposed estimation model and the improved DR potential estimation model across multiple cases.

Abstract:The increasing integration of intermittent renewable energy sources into distribution networks has exerted significant pressure on the frequency regulation of power systems. Meanwhile, integrating small-capacity battery energy storage systems into distribution network is a growing trend in the construction of virtual power plants (VPPs), which offer great potential advantages in improving the system frequency regulation capabilities. However, the process of power dispatch for VPPs may be hindered by imperfections in the communication network, which affects their frequency control performance. Simultaneously, the economic benefits associated with their frequency control services are often overlooked. As such, we propose a co-design method of power dispatch with dynamic power regulation and communication transmission optimization for frequency control in VPPs. First, a joint design scheme of power dispatch and routing optimization under cloud-edge collaborations is proposed. This scheme encompasses a power dispatch method considering the influences of communication network and a routing optimization policy based on graph convolutional neural networks, both of which are designed to ensure the accurate and real-time frequency control service. Further, we propose a dynamic power regulation strategy under edge-edge collaborations. Specifically, according to the established correction control objective, an adaptive distributed auction algorithm (ADAA) based dynamic power regulation control method is designed to determine the optimal regulation power of VPPs, thereby improving the economic benefits of frequency control service. Finally, the simulation results validate the feasibility and superiority of the proposed co-design method for frequency control.

Abstract:The increasing integration of renewable energy sources poses great challenges to the power system frequency security. However, the existing electricity market mechanism lacks integration and incentives for emerging frequency regulation (FR) resources such as wind power generators (WPGs), which may reduce their motivation to provide frequency support and further deteriorate the frequency dynamics. In this paper, a market scheduling and pricing method for comprehensive frequency regulation services (FRSs) is proposed. First, a modeling approach for flexible FR capabilities of WPGs is proposed based on the mechanism of inertia control and power reserve control. Subsequently, considering the differences in inverter control strategies, a novel system frequency response model with grid-following and grid-forming inverters is established. Combined with the automatic generation control, the frequency security constraints of the whole FR process are derived, and integrated into the market scheduling model to co-optimize the energy and FRSs. Finally, by distinguishing the contributions of various types of resources in different FR stages, a differentiated pricing scheme is proposed to incentivize producers with various regulation qualities to provide FRSs. The effectiveness of the proposed method is verified on the modified IEEE 6-bus system and the IEEE RTS-79 system.

Abstract:Advanced management algorithms are required in modern power systems to sustain energy supply with the highest availability and lowest cost. These algorithms need to be capable of not only maintaining scalability, tractability, and privacy, but also enabling the utilization of grid-edge aggregated flexibilities in transmission systems. This paper proposes a distributed hierarchical transactive energy management (TEM) scheme to manage peak load and line congestion problems using connected and aggregated flexibilities. In the scheme, resource owners can privately solve their respective preference problems and send their scheduled power to the corresponding node operator (NO). Afterward, NOs solve a coordination problem to harmonize the actions of resource owners at the same node. Meanwhile, the independent system operator (ISO) updates control signals to steer the scheduled power to a feasible and optimal point. To accomplish all these, a hybrid decomposition approach is further proposed based on consensus+exchange alternating direction method of multipliers (CE-ADMM) and dual decomposition (DD) (CE-ADMM+DD). Besides, a dynamically constrained cutting plane (DC-CP) update algorithm is evolved to control the feasibility condition and minimize sensitivity to initialization. The proposed hybrid decomposition approach is verified and its performance is compared with other reported approaches. Application to various networks verifies its scalability, enhanced accuracy, and convergence speed.

Abstract:Data centers are promising demand-side flexible resources that can provide frequency regulation services to power grids. While most existing studies focus on individual data centers, coordinating multiple geo-distributed data centers can significantly enhance operational flexibility and market participation. However, the inherent uncertainty in both data center workloads and regulation signals pose significant challenges to maintaining effective operations, let alone determining regulation capacity offerings. To address these challenges, this paper proposes a coordinated bidding strategy for electricity purchases and regulation capacity offerings for multiple geo-distributed data centers in electricity markets. This strategy expands the feasible region of operational decisions, including workload dispatch, server activation, and cooling behaviors. To enhance the participation of data centers in frequency regulation services under uncertainty, chance-constrained programming is adopted. This paper presents explicit models for these uncertainties involved, starting with the Poisson-distributed workloads and then addressing the unpredictable regulation signals. Numerical experiments based on real-world datasets validate the effectiveness of the proposed strategy compared with state-of-the-art strategies.

Abstract:Unregulated naked selling of virtual power plants (VPPs) in day-ahead markets poses inherent risks to grid security and market fairness. This paper proposes a joint electricity-reserve trading model for VPPs as a strategic measure to mitigate the negative impacts of naked selling. This model systematically evaluates the economic advantages and risks of naked selling, utilizing metrics such as user comfort and conditional value at risk (CVaR). Furthermore, a sophisticated combination of a data-driven level-set fuzzy approach and advanced algorithms, including support vector quantile regression (SVQR) and kernel density estimation (KDE), is employed to quantify the uncertainties related to prices and reserve activation precisely. The results of case studies demonstrate that integrating default penalties within the proposed trading model diminishes the overall revenue of VPPs engaging in naked selling, thereby serving as a robust decision for mitigating the adverse effects of the naked selling of VPPs.

Abstract:With the increasing number of distributed flexible resources with energy storage capabilities in virtual power plants (VPPs), the traditional market clearing model that only includes quantity and price bids cannot fully unlock their potential flexibility. In light of this, we propose a market clearing model for energy-constrained virtual power plants (EC-VPPs) based on distributionally robust chance-constrained optimization (DRCCO) with moment information. Furthermore, to address the uncertainty of EC-VPPs in the electricity market, a pricing strategy for EC-VPPs is proposed. This strategy helps quantify the impact of uncertainty in EC-VPPs on the system economy. The proposed market clearing model is reformulated as a tractable mixed-integer second-order cone programming (MISOCP) problem via a two-sided distributionally robust chance-constrained convex reformulation method. Numerical simulations verify that the proposed pricing strategy offers fair incentives for both reserve providers and uncertain sources, and delivers an effective market mechanism for the EC-VPPs.

Abstract:The scale of distributed energy resources is increasing, but imperfect business models and value transmission mechanisms lead to low utilization ratio and poor responsiveness. To address this issue, the concept of cleanness value of distributed energy storage (DES) is proposed, and the spatiotemporal distribution mechanism is discussed from the perspectives of electrical energy and cleanness. Based on this, an evaluation system for the environmental benefits of DES is constructed to balance the interests between the aggregator and the power system operator. Then, an optimal low-carbon dispatching for a virtual power plant (VPP) with aggregated DES is constructed, wherein energy value and cleanness value are both considered. To achieve the goal, a green attribute labeling method is used to establish a correlation constraint between the nodal carbon potential of the distribution network (DN) and DES behavior, but as a cost, it brings multiple nonlinear relationships. Subsequently, a solution method based on the convex envelope (CE) linear reconstruction method is proposed for the multivariate nonlinear programming problem, thereby improving solution efficiency and feasibility. Finally, the simulation verification based on the IEEE 33-bus DN is conducted. The simulation results show that the multidimensional value recognition of DES motivates the willingness of resource users to respond. Meanwhile, resolving the impact of DES on the nodal carbon potential can effectively alleviate overcompensation of the cleanness value.

Abstract:In this paper, a novel multi-objective optimization model of integrated energy systems (IESs) is proposed based on the ladder-type carbon emission trading mechanism and refined load demand response strategies. First, the carbon emission trading mechanism is introduced into the optimal scheduling of IESs, and a ladder-type carbon emission cost calculation model based on rewards and penalties is established to strictly control the carbon emissions of the system. Then, according to different response characteristics of electric load and heating load, a refined load demand response model is built based on the price elasticity matrix and substitutability of energy supply mode. On these basis, a multi-objective optimization model of IESs is established, which aims to minimize the total operating cost and the renewable energy source (RES) curtailment. Finally, based on typical case studies, the simulation results show that the proposed model can effectively improve the economic benefits of IESs and the utilization efficiency of RESs.

Abstract:This paper proposes an online framework to characterize demand response (DR) over time. The proposed framework facilitates obtaining and updating the daily consumption patterns of customers. The essential concept of response profile class (RPC) is introduced for characterization and complemented by the measure of the variability in customer behavior. This paper uses a modified version of the incremental clustering by fast search and find of density peaks (CFSFDP) algorithm for daily profiles, considering the multivariate normal kernel density estimator and incremental forms of the Davies-Bouldin (iDB) and Xie-Beni (iXB) validity indices. Case studies conducted using real-world and simulated daily profiles of residential and commercial Chilean end-users have demonstrated how the proposed framework can continuously characterize DR. The proposed framework is proven to achieve realistic customer models for effective energy management by estimating the customer response to price signals at the distribution system operator (DSO) level.

Abstract:Electrical water heaters (EWHs) are important candidates to provide demand-response services. The traditional optimization method for EWHs focuses on the optimization of the electricity consumption, without considering the shifting potential of the water-use activities. This paper proposes an optimization method for EWHs considering the shifting potentials of both the electricity consumption and water-use activities. Considering that the water-use activities could be monolithically shifted, the shifting model of the water-use activities was developed. In addition to the thermodynamic model of the EWH, the optimal scheduling model of the EWH was developed and solved using mixed-integer linear programming. Case studies were performed on a single EWH and aggregate EWHs, demonstrating that the proposed method can shift the water-use activities and therefore increase the load-shifting potential of the EWHs.

Abstract:Demand response transactions between electric consumers, load aggregators, and the distribution network manager based on the “combination of price and incentive” are feasible and efficient. However, the incentive payment of demand response is quantified based on private information, which gives the electric consumers and load aggregators the possibility of defrauding illegitimate interests by declaring false information. This paper proposes a method based on Vickrey-Clark-Groves (VCG) theory to prevent electric consumers and load aggregators from taking illegitimate interests through deceptive declaration in the demand response transactions. Firstly, a demand response transaction framework with the price-and-incentive combined mode is established to illustrate the deceptive behavior in the demand response transaction. Then, the idea for eradicating deceptive declarations based on VCG theory is given, and a detailed VCG-based mathematical model is constructed following the demand response transaction framework. Further, the proofs of incentive compatibility, individual rationality, cost minimization, and budget balance of the proposed VCG-based method are given. Finally, a modified IEEE 33-node system and a modified IEEE 123-node system are used to illustrate and validate the proposed method.

Abstract:Due to the effects of windless and sunless weather, new power systems dominated by renewable energy sources experience power supply shortages, which lead to severe electricity shortages. Because of the insufficient proportion of controllable thermal power in these systems, this problem must be addressed from the load side. This study proposes an orderly power utilization (OPU) method with load as the primary dispatching object to address the problem of severe electricity shortages. The principles and architecture of the new urban power grid (NUPG) OPU are proposed to complete the load curtailment task and minimize the effects on social production and daily life. A flexible load baseline division method is proposed that considers the effects of factors such as gross domestic product, pollutant emission, and carbon emission to increase the flexibility and applicability of the proposed method. In addition, an NUPG OPU model based on the load baseline is proposed, in which the electric quantity balance aggregator (EQBA) serves as a regular participant in the OPU and eliminates the need for other user involvement within its capacity range. The electric quantity reserve aggregator (EQRA) functions as a supplementary participant in the OPU and primarily performs the remaining tasks of the EQBA. The electric power balance aggregator primarily offsets the power fluctuations of the OPU. Case studies demonstrate the effectiveness and superiority of the proposed model in ensuring the completion of the load curtailment task, enhancing the flexibility and fairness of OPUs, and improving the applicability of the proposed method.