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Flexible load at the demand-side has been regarded as an effective measure to cope with volatile distributed renewable generations. To unlock the demand-side flexibility, this paper proposes a peer-to-peer energy sharing mechanism that facilitates energy exchange among users while preserving privacy. We prove the existence and partial uniqueness of the energy sharing market equilibrium and provide a centralized optimization to obtain the equilibrium. The centralized optimization is further linearized by a convex combination approach, turning into a multi-parametric linear program (MP-LP) with renewable output deviations being the parameters. The flexibility requirement of individual users is calculated based on this MP-LP. To be specific, an adaptive vertex generation algorithm is established to construct a piecewise linear estimator of the optimal total cost subject to a given error tolerance. Critical regions and optimal strategies are retrieved from the obtained approximate cost function to evaluate the flexibility requirement. The proposed algorithm does not rely on the exact characterization of optimal basis invariant sets and thus is not influenced by model degeneracy, a common difficulty faced by existing approaches. Case studies validate the theoretical results and show that the proposed method is scalable., Comment: 11 pages, 10 figures

Demand response (DR) is an important technique to explore the demand-side flexibility. The wide deployment of smart meters makes it possible to quantify the baseline load. As an intermediate agent, demand response aggregator needs to obtain the aggregated baseline load (ABL) for the DR event. Previous studies about the household level estimation focus on the estimation method. However, for ABL estimation, customer division is an important issue. A major limitation is the mismatch between the objectives of segmentation and estimation. Therefore, this paper proposes a new closed-loop method for estimating the ABL, which utilizes the contextual bandit with policy gradient to link the segmentation with the estimation. As such, the ABL estimation accuracy can guide the segmentation to divide the customers. The segmentation and estimation optimize collaboratively to improve the ABL estimation accuracy. An ensemble method for combining network’s weights during the training process is proposed. Moreover, a pre-and post-event adjustment method is developed to further improve the estimation accuracy. Comprehensive comparisons demonstrate the proposed method can achieve the best estimation performance with regard to the MAPE and RMSE. It improves the estimation accuracy by 7% in terms of MAPE, and 11% in terms of RMSE.

Transactive energy (TE) is a viable framework to tackle the load-generation mismatch in energy systems with high penetration of renewable energy resources (RERs). In this paper, we propose a TE framework for prosumers with heating, ventilation, and air conditioning (HVAC) systems to address real-time power shortage in a residential microgrid. Our framework consists of two phases. First, to mitigate load-generation mismatch, we develop an online appliance scheduling method to determine the optimal operation schedule of each prosumer’s appliances. In particular, we apply receding horizon optimization (RHO) to tackle the load and renewable generation uncertainties and to better match the real-time power consumption of the appliances with the priorly-purchased power from the day-ahead market. Second, in case that there still exists power shortage at the microgrid level, a TE market based on pay-as-market clearing price (MCP) is proposed among prosumers to reduce the power consumption of their HVAC systems. We capture the competition among the participating prosumers as a non-cooperative game and develop an algorithm to achieve the Nash equilibrium, while considering prosumers’ willingness to participate in the TE market. Extensive simulations are performed to demonstrate the efficiency of our proposed TE framework.

Deploying distributed renewable energy at the demand side is an important measure to implement a sustainable society. However, the massive small solar and wind generation units are beyond the control of a central operator. To encourage users to participate in energy management and reduce the dependence on dispatchable resources, a peer-to-peer energy sharing scheme is proposed which releases the flexibility at the demand side. Every user makes decision individually considering only local constraints; the microgrid operator announces the sharing prices subjective to the coupling constraints without knowing users' local constraints. This can help protect privacy. We prove that the proposed mechanism can achieve the same disutility and flexibility as centralized dispatch, and develop an effective modified best response based algorithm for reaching the market equilibrium. The concept of absorbable region is presented to measure the operating flexibility under the proposed energy sharing mechanism. A linear programming based polyhedral projection algorithm is developed to compute that region. Case studies validate the theoretical results and show that the proposed method is scalable., 12 pages, 16 figures, accepted by IEEE Transactions on Smart Grid

Optimizing the operation of heating, ventilation, and air-conditioning (HVAC) systems is a challenging task that requires the modeling of complex nonlinear relationships among the HVAC load, indoor temperature, and outdoor environment. This article proposes a new strategy for optimal operation of an HVAC system in a commercial building. The system for indoor temperature control is divided into three sub-systems, each of which is modeled using an artificial neural network (ANN). The ANNs are then interconnected and integrated into an optimization problem for temperature set-point scheduling. The problem is reformulated to determine the optimal set-points using a deterministic search algorithm. After the optimal scheduling has been initiated, the ANNs undergo online learning repeatedly, mitigating overfitting. Case studies are conducted to analyze the performance of the proposed strategy, compared to strategies with a pre-determined temperature set-point, an ideal physics-based building model, and other types of machine learning-based modeling and scheduling methods. The case study results confirm that the proposed strategy is effective in terms of the HVAC energy cost, practical applicability, and training data requirements.

This article addresses the optimal bidding strategy problem of a virtual power plant (VPP) participating in the day-ahead (DA), real-time (RT) and spinning reserve (SR) markets (SRMs). The VPP comprises a number of dispatchable energy resources (DERs), renewable energy resources (RESs), energy storage systems (ESSs) and a number of customers with flexible demand. A two-stage risk-constrained stochastic problem is formulated for the VPP scheduling, where the uncertainty lies in the energy and reserve prices, RESs production, load consumption, as well as calls for reserve services. Based on this model, the VPP bidding/offering strategy in the DA market (DAM), RT market (RTM) and SRM is decided aiming to maximize the VPP profit considering both supply and demand-sides (DS) capability for providing reserve services. On the other hand, customers participate in demand response (DR) programs by using load curtailment (LC) and load shifting (LS) options as well as by providing reserve service to minimize their consumption costs. The proposed model is implemented on a test VPP and the optimal decisions are investigated in detail through a numerical study. Numerical simulations demonstrate the effectiveness of the proposed scheduling strategy and its operational advantages and the computational effectiveness.

Wind power curtailment (WPC) occurs because of the non-correlation between wind power generation (WPG) and load, and also due to the fast sub-hourly variations of WPG. Recently, advances in energy storage technologies facilitate the use of bulk energy storage units (ESUs) to provide the ramping required to respond to fast sub-hourly variations of WPGs. To minimize the sub-hourly WPC probability, this paper addresses a generic continuous-time risk-based model for sub-hourly scheduling of energy generating units and bulk ESUs in the day-ahead unit commitment (UC) problem. Accordingly, the Bernstein polynomials are hosted to model the continuous-time risk-based UC problem with ESU constraints. Also, the proposed continuous-time risk-based model ensures that the generating units and ESUs track the sub-hourly variations of WPG, while the load and generation are balanced in each sub-hourly intervals. Finally, the performance of the proposed model is demonstrated by simulating the IEEE 24-bus Reliability and Modified IEEE 118-bus test systems.

This paper studies the role of electricity demand response program (EDRP) in the co-operation of the electric power systems and the natural gas transmission system to facilitate integration of wind power generation. It is known that time-based uncertainty modeling has a critical role in co-operation of electricity and gas systems. Also, the major limitation of the hourly discrete time model (HDTM) is its inability to handle the fast sub-hourly variations of generation sources. Accordingly, in this paper, this limitation has been solved by the operation of both energy systems with a continuous time model (CTM). Also, a new fuzzy information gap decision theory (IGDT) approach has been proposed to model the uncertainties of the wind energy. Numerical results on the IEEE Reliability Test System (RTS) demonstrate the benefits of applying the continuous-time EDRP to improve the co-scheduling of both natural gas and electricity systems under wind power generation uncertainty.

Large-scale compressed air energy storage (CAES) is conventionally used in power systems. However, application of CAESs at the distribution level is limited because of differences in design and efficiency. On the other hand, application of electrical batteries suited for distribution networks (DNs) faces also challenges from high investment cost and significant degradation. In this regard, this paper presents the participation of an active distribution system equipped with a small-scale CAES (SCAES) in the day-ahead wholesale market. To make CAES applicable to DNs, thermal-electrical setting design of the SCAES coupled with a packed-bed heat exchanger is adopted in the operation of the grid, where SCAES performs as an energy storage for DNs to surpass existing deficiencies of battery banks. The electrical/thermal conversion rate has been modeled for the SCAES operation. Moreover, the operation strategy of the SCAES is optimally coordinated with an electric vehicle charging station (EVCS) as an alternative energy storage technology in deregulated DNs. To make EVCS simulation more realistic, Gaussian Copula probability distribution function is used to model the behavior of the EVCS. The results obtained from different case studies confirm the value of SCAES as a reliable energy storage technology for DNs.

The decision making framework in power systems has changed due to presence of distributed energy resources (DERs). These resources are installed in distribution networks to meet demand locally. Therefore, distribution companies (Discos) are able to supply energy through these resources to meet their demand at a minimum operation cost. In this framework, the Disco will change its role in the wholesale energy market from price taker to price maker. DERs can provide reserve in their normal operation; this facilitates the provision of reserves by the Disco in the wholesale reserve market. Therefore, in this paper, the strategic behavior of a Disco in wholesale energy and reserve markets is modeled as a bi-level optimization problem. In the proposed model, the operation problem of the Disco and the independent system operator are modeled in the upper- and lower-level problems, respectively. Karush–Kuhn–Tucker conditions and duality theory are used to transform the proposed nonlinear bi-level problem to a linear single level one. Numerical studies show the effectiveness of the proposed model and its solution methodology.

A real-time price (RTP)-based automatic demand response (ADR) strategy for PV-assisted electric vehicle (EV) Charging Station (PVCS) without vehicle to grid is proposed. The charging process is modeled as a dynamic linear program instead of the normal day-ahead and real-time regulation strategy, to capture the advantages of both global and real-time optimization. Different from conventional price forecasting algorithms, a dynamic price vector formation model is proposed based on a clustering algorithm to form an RTP vector for a particular day. A dynamic feasible energy demand region (DFEDR) model considering grid voltage profiles is designed to calculate the lower and upper bounds. A deduction method is proposed to deal with the unknown information of future intervals, such as the actual stochastic arrival and departure times of EVs, which make the DFEDR model suitable for global optimization. Finally, both the comparative cases articulate the advantages of the developed methods and the validity in reducing electricity costs, mitigating peak charging demand, and improving PV self-consumption of the proposed strategy are verified through simulation scenarios.

Modernization of electricity networks is currently being carried out using the concept of the smart grid; hence, the active participation of end-user consumers and distributed generators will be allowed in order to increase system efficiency and renewable power accommodation. In this context, this paper proposes a comprehensive methodology to optimally control lead-acid batteries operating under dynamic pricing schemes in both independent and aggregated ways, taking into account the effects of the charge controller operation, the variable efficiency of the power converter, and the maximum capacity of the electricity network. A genetic algorithm is used to solve the optimization problem in which the daily net cost is minimized. The effectiveness and computational efficiency of the proposed methodology is illustrated using real data from the Spanish electricity market during 2014 and 2015 in order to evaluate the effects of forecasting error of energy prices, observing an important reduction in the estimated benefit as a result of both factors: 1) forecasting error and 2) power system limitations.

In this paper, the optimal operation of a neighborhood of smart households in terms of minimizing the total energy procurement cost is analyzed. Each household may comprise several assets such as electric vehicles, controllable appliances, energy storage and distributed generation. Bi-directional power flow is considered both at household and neighborhood level. Apart from the distributed generation unit, technological options such as vehicle-to-home and vehicle-to-grid are available to provide energy to cover self-consumption needs and to inject excessive energy back to the grid, respectively. The energy transactions are priced based on the net-metering principles considering a dynamic pricing tariff scheme. Furthermore, in order to prevent power peaks that could be harmful for the transformer, a limit is imposed to the total power that may be drawn by the households. Finally, in order to resolve potential competitive behavior, especially during relatively low price periods, a simple strategy in order to promote the fair usage of distribution transformer capacity is proposed.

Erdinç, Ozan (Arel Author), As the smart grid solutions enable active consumer participation, demand response (DR) strategies have drawn much interest in the literature recently, especially for residential areas. As a new type of consumer load in the electric power system, electric vehicles (EVs) also provide different opportunities, including the capability of utilizing EVs as a storage unit via vehicle-to-home (V2H) and vehicle-to-grid (V2G) options instead of peak power procurement from the grid. In this paper, as the main contribution to the literature, a collaborative evaluation of dynamic-pricing and peak power limiting-based DR strategies with a bi-directional utilization possibility for EV and energy storage system (ESS) is realized. A mixed-integer linear programming (MILP) framework-based modeling of a home energy management (HEM) structure is provided for this purpose. A distributed small-scale renewable energy generation system, the V2H and V2G capabilities of an EV together with two-way energy trading of ESS, and different DR strategies are all combined in a single HEM system for the first time in the literature. The impacts of different EV owner consumer preferences together with the availability of ESS and two-way energy trading capabilities on the reduction of total electricity prices are examined with case studies.

This paper describes the development of a multifunction control strategy for the stable operation of distributed generation (DG) units during the integration with the power grid. The proposed control model is based on direct Lyapunov control (DLC) theory and provides a stable region for the proper operation of DG units during the integration with the power grid. The compensation of instantaneous variations in the reference current components in ac-side and dc-voltage variations in the dc-side of the interfacing system are adequately considered in this control plan, which is the main contribution and novelty of this paper in comparison with previous control strategies. Utilization of the DLC technique in DG technology can confirm the continuous injection of maximum active power in fundamental frequency from the DG source to the power grid, compensating all the reactive power and harmonic current components of grid-connected loads through the integration of DG link into the grid. Application of this concept in smart grids system can guarantee to reduce the stress on the utility grid during the peak of energy demand. Simulation and experimental test results are presented to demonstrate the proficiency and performance of the proposed DLC technique in DG technology.

The articles in this special section focus on the topic of real time demand response applications.