Optimal DR through HVAC loads in distribution systems hosting large wind generation

The increasing integration of distributed energy resources (DERs), such as photovoltaics (PVs) and smart buildings into distribution systems complicate power system operation and controls. This paper proposes a coordinated optimal control strategy for PV inverters and Heating, ventilation, and air conditioning (HVAC) loads in smart buildings to minimize the total network loss in a distribution system. For the HVAC units, we enforce minimum on and off time constraints to avoid frequent switching that can degrade the unit. The proposed control will dispatch optimal control signals of active and reactive power to PV inverters and on/off commands to HVAC units while maintaining the nodal voltage within a secure range and the temperature of HVAC units at a comfort level. The simulation results on a modified IEEE 33-node distribution system demonstrate that the proposed coordinated control scheme can reduce the network loss.

Demand response offers the possibility of altering the profile of power consumption of individual buildings or building districts, i.e., microgrids, for economic return. There is significant potential of demand response in enabling flexibility via advanced grid management options, allowing higher renewable energy penetration and efficient exploitation of resources. Demand response and distributed energy resource dynamic management are gradually gaining importance as valuable assets for managing peak loads, grid balance, renewable energy source intermittency, and energy losses. In this paper, the potential for operational optimization of a heating, ventilation, and air conditioning (HVAC) system in a smart near-zero-energy industrial building is investigated with the aid of a genetic algorithm. The analysis involves a validated building energy model, a model of energy cost, and an optimization model for establishing HVAC optimum temperature set points. Optimization aims at establishing the trade-off between the minimum daily cost of energy and thermal comfort. Predicted mean vote is integrated in the objective function to ensure thermal comfort requirements are met.

This paper introduces an improved real-time Model Predictive Climate Control (MPCC) technique to reduce power consumption in Heating, Ventilation and Air Conditioning (HVAC) system of the Battery Electric Vehicles (BEVs). In the presented control technique, the fan, heating and cooling power usage is optimized considering the effect of the ambient temperature. Moreover, the need of a physical temperature measurement sensor is eliminated using an ambient temperature observer. In a typical BEV, the HVAC load is significant and it influences the overall vehicle performance and driving range. Therefore, a real-time control system capable of maintaining desired cabin temperature while achieving maximum HVAC efficiency is highly desirable. In the proposed MPCC, the optimum split of the battery power between the heating\cooling and the fan power is identified as a function of ambient temperature through an offline optimization process and used in the form of a lookup-table for real-time implementation. It is demonstrated that the proposed MPCC improves energy consumption efficiency of the entire HVAC system up to 5% by dynamically adapting to the variations in the ambient temperature. Furthermore, owing to the online temperature observation process, the proposed HVAC control system allows elimination of ambient temperature sensor and corresponding maintenance efforts. In this paper, the performance of the proposed MPCC is evaluated over a single-zone HVAC model of a BEV in MATLAB\Simulink <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> environment.

This paper is concerned with the promising benefits of the incorporation of demand response (DR) scheme in residential end-users, but considering the AC distribution network's constraints and several retail companies or Local Service Entities (LSEs) participating in the retail market. This residential DR is modeled as an OPF problem run by the Operator of the distribution network, with the aggregated information collected by each retail company from their clients. We use a simple model and formulate the problem as a dynamic program that maximizes social welfare. The primal-dual method used to decouple some constraints and optimize the system, preserves the integrity of the private information of each participant and captures its preferences and constraints.

Residential demand response (DR) services have the great potential of growth, yet they are not adopted extensively. A comprehensive residential DR framework proposed in [2] connects utilities with DR participants via mobile apps, supports multiple DR programs, and enables fine-granulated control. In this paper, we focus on mathematical modeling and solutions for economically optimal operation for popular DR programs so that utilities can appropriately select and combine those programs to meet their needs. Performances of multiple DR programs are evaluated via simulations based on real-world data.

Demand response (DR) involves economic incentives aimed at balancing energy demand during critical demand periods. In doing so DR offers the potential to assist with grid balancing, integrate renewable energy generation and improve energy network security. Buildings account for roughly 40% of global energy consumption. Therefore, the potential for DR using building stock offers a largely untapped resource. Heating, ventilation and air conditioning (HVAC) systems provide one of the largest possible sources for DR in buildings. However, coordinating the real-time aggregated response of multiple HVAC units across large numbers of buildings and stakeholders poses a challenging problem. Leveraging upon the concepts of Industry 4.0, this paper presents a large-scale decentralized discrete optimization framework to address this problem. Specifically, the paper first focuses upon the real-time dispatch problem for individual HVAC units in the presence of a tertiary DR program. The dispatch problem is formulated as a non-linear constrained predictive control problem, and an efficient dynamic programming (DP) algorithm with fixed memory and computation time overheads is developed for its efficient solution in real-time on individual HVAC units. Subsequently, in order to coordinate dispatch among multiple HVAC units in parallel by a DR aggregator, a flexible and efficient allocation/reallocation DP algorithm is developed to extract the cost-optimal solution and generate dispatch instructions for individual units. Accurate baselining at individual unit and aggregated levels for post-settlement is considered as an integrated component of the presented algorithms. A number of calibrated simulation studies and practical experimental tests are described to verify and illustrate the performance of the proposed schemes. The results illustrate that the distributed optimization algorithm enables a scalable, flexible solution helping to deliver the provision of aggregated tertiary DR for HVAC systems for both aggregators and individual customers. The paper concludes with a discussion of future work.

Nowadays, demand response (DR) is used to modify the real‐time load curve either directly through incentive‐based centralised control or indirectly through inducing a particular load pattern by time‐varying pricing schemes. Various DR schemes are available which encourage the participation of industrial, commercial and residential consumers in system operation. This study investigates the potential of residential consumers’ DR participation in reducing Deviation Settlement Charge (DSC) under frequency‐linked pricing environment. An incentive‐based DR model for household devices by categorising them into shiftable and interruptible devices; including customer‐side Battery Energy Storage System has been presented. A particular category of devices participates in a centrally initiated DR program depending upon the real‐time grid frequency. The simulations are carried out on a realistic system of Indian distribution utility having a large percentage of residential consumers. It has been observed that the residential community DR participation reduces the DSCs substantially under real‐time frequency conditions.

The quantification of heating, ventilation, and air condition (HVAC) system flexibility is critical to the operations of both the grid and buildings in demand response (DR) programs. However, the flexibility quantification is challenging due to the non-linearity and non-convexity of thermal dynamics associated with HVAC components. This paper proposes a novel HVAC flexibility quantification method based on a semidefinite programming (SDP) formulation. The SDP is reformulated from the non-convex problem of HVAC power optimization, and can be solved efficiently in real-time. The physics-based HVAC model is incorporated to ensure the reliability and accuracy of solutions. The quantification results are organized into an HVAC flexibility table that can provide response strategies on adjusting HVAC setpoints in response to the grid signals received. The developed response strategies minimize occupant discomfort while satisfying grid requirements. A case study of a test building model is carried out to illustrate the flexibility quantification framework and compares the performance of two DR strategies.

Flexible demand response (DR) resources can be leveraged to accommodate the stochasticity of some distributed energy resources. This paper develops an online learning approach that continuously estimates price sensitivities of residential DR participants and produces such price signals to the DR participants that ensure a desired level of DR capacity. The proposed learning approach incorporates the dispatch decisions on DR resources into the distributionally robust chance-constrained optimal power flow (OPF) framework. This integration is shown to adequately remunerate DR resources and co-optimize the dispatch of DR and conventional generation resources. The distributionally robust chance-constrained formulation only relies on empirical data acquired over time and makes no restrictive assumptions on the underlying distribution of the demand uncertainty. The distributional robustness also allows for robustifying the otpimal solution against systematically misestimating empirically learned parameters. The effectiveness of the proposed learning approach is shown via numerical experiments. The paper is accompanied by the code and data supplement released for public use.

Performance evaluation of conventional demand response at building-group-level under different electricity pricings

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.

Residential electrical demand response (DR) offers the prospect of reducing the environmental impact of electricity use, and also the supply costs. However, the relatively small loads and numerous actors imply a large effort: response ratio. Residential DR may be an essential part of future smart grids, but how viable is it in the short to medium term? This paper reviews some DR concepts, then evaluates the propositions that households in cool temperate climates will be in a position to contribute to grid flexibility within the next decade, and that that they will allow some automated load control. Examples of demand response from around the world are discussed in order to assess the main considerations for cool climates. Different tariff types and forms of control are assessed in terms of what is being asked of electricity users, with a focus on real-time pricing and direct load control in energy systems with increasingly distributed resources. The literature points to the significance of thermal loads, supply mix, demand-side infrastructure, market regulation, and the framing of risks and opportunities associated with DR. In concentrating on social aspects of residential demand response, the paper complements the body of work on technical and economic potential.

In this paper, a three-stage stochastic bi-level optimization framework is presented for optimal participation of wind power producers (WPPs) in day-ahead (DA), intraday, and balancing markets. In this framework, to leverage demand response (DR) services, a peer-to-peer (P2P) energy trading platform is implemented that allows local load aggregators (LAs) to contribute to the intraday markets improving both LAs’ and WPP’s benefits. Participating in the intraday DR exchange (IDRX) market enables WPP to purchase DR services from LAs, to reduce the penalty cost on the deviation between the day-head bidding and the real-time dispatch. A Stackelberg game for the bi-level decision-making model captures the conflict of interests between the WPP and LAs, in which, the upper level seeks to maximize WPP profit, while the lower level aims to maximize LAs’ economic surplus. The bi-level model is converted into its equivalent single-level mixed-integer quadratic problem (MIQP) employing the Karush-Kuhn-Tucker (KKT) conditions and strong duality theorem. Simulation results show that participation of the WPP in the IDRX market and employing spinning reserve and DR services for compensating the uncertainties are greatly dependent on its risk preferences and increase its expected profit in all conditions, significantly.

The necessity to seamlessly integrate emerging smart grid (SG) technologies to the electric distribution system has encouraged the development of demand-side management (DSM) and Demand Response (DR) programs. In particular, DR aggregators will play an important role in the new operational paradigm of SG. To propose a new three-phase optimal dispatch model for multiple DR aggregators. The model can consider several DR service providers by means of the explicit inclusion of their characteristics, in the form of cost functions and capacity, in an optimal dispatch model for DR support at the distribution system level. Hence, this proposal helps to eliminate any possible uncertainty about the provision of DR services by improving the traditional hierarchical scheme adopted based on prices issued by the independent system operator. By means of the unbalanced distribution system modeling, DR involvement of single- and three-phase consumers is considered avoiding aggravating the asymmetric balancing between phases, as opposed to traditional positive-sequence DR approaches. The model is implemented and solved combining the two state-of-the-art computational tools, Matlab® and OpenDSS. The former is used to solve the optimization problem, whereas the latter is used to perform numerical simulations of three-phase unbalanced power flow; both tools allow a straightforward model implementation resulting in a tool easily modified and updated. The effectiveness of the proposed approach is numerically demonstrated using the IEEE 13- and 123-node test feeders, in which undesirable operating scenarios are corrected by the implementation of the optimal dispatch of DR resources in very short computational times. Based on the results, it is shown how the ISO is capable to request DR considering several aggregators competing at the distribution system level. Finally, the loss reduction has been included in the objective function, showing that the proposal optimally dispatches the DR aggregators to conveniently minimize the ISO’s operational costs.

Studies have demonstrated that Demand Response (DR) can significantly influence the effectiveness and reliability of future intelligent distribution networks. Despite the increasing deployment of wind energy, the issue of curtailment continues to pose a challenge for utilities that have a high penetration of wind power. This study aims to propose an effective approach for utilizing DR to improve the operating status of distribution networks while simultaneously reducing curtailed wind energy. Thus, considering the curtailed wind energy, this paper proposes a wind curtailment reduction DRP (WCR-DRP) to modify the demand pattern in response to critical times when wind curtailment may occur due to generator ramp limits or minimum output power. In this study, to implement power system operation, an optimal power flow (OPF) problem is utilized while considering the availability of demand response (DR) programs, and the power flow and operational constraints related to the system are considered . Formulating the optimization problem with two objective functions, including minimizing overall costs and reducing wind power curtailment, is achieved. The ?-constraint method resolves this optimization problem. Finally, the suggested model is employed over the enhanced IEEE 33-bus test system to evaluate its efficacy. Four scenarios are conducted, examining various operational parameters to show the practicality of the suggested approach. Outcomes show that suggested model significantly enhances the operation of the electrical distribution system while establishing optimal employment of wind power.