Load Serving Entities Research Articles

Price responsiveness of solar and wind capacity demands

Accurate estimates of the price responsiveness of residential, commercial, and industrial electricity demands are essential for energy policy modelling, integrated resource planning, and determining a competitive wholesale electricity market's generation levels, prices, and capacity investments. Hence, we estimate the own-price elasticities of solar and wind capacity demands of a load serving entity (LSE) that provides retail electricity service, thereby answering two interrelated research questions: (1) does solar capacity demand far exceed wind capacity demand? and (2) are solar and wind capacity demands price-elastic? Inspired by the theory of input demand under input price uncertainty, our innovative methodology integrates (a) wholesale spot energy price forecasts by time of day; (b) pseudo data found by minimizing a LSE's annual risk-adjusted budget for procuring solar and wind capacities; and (c) econometric analysis of (b) to estimate the extent of substitutability between solar and wind capacities and the own-price elasticities of solar and wind capacity demands. Using Texas as an illustrative example, we find that when solar and wind power purchase agreements have similar energy prices, solar capacity demand is approximately four times wind capacity demand. Further, the own-price elasticity estimates are −5.34 for solar capacity demand and −5.65 for wind capacity demand. As a result, solar and wind capacity demands tend to substantially grow (shrink) in response to declining (rising) solar and wind energy prices. This lends support to proposals to raise solar and wind energy prices for mitigating the adverse effects of large-scale variable renewable energy development on an electric grid's efficient operation and system reliability. However, adopting such proposals also slows the grid's pace of decarbonization, thus underscoring the policy and regulatory challenges in the quest for a clean and sustainable electricity future. Hence, our policy recommendation of price managing solar and wind capacity demands is a topic of policy debate that deserves the attention of an electric grid's stakeholders.

A Human-Centered Power Conservation Framework based on Reverse Auction Theory and Machine Learning

Extreme outside temperatures resulting from heat waves, winter storms, and similar weather-related events trigger the Heating Ventilation and Air Conditioning (HVAC) systems, resulting in challenging, and potentially catastrophic, peak loads. As a consequence, such extreme outside temperatures put a strain on power grids and may thus lead to blackouts. In order to avoid the financial and personal repercussions of peak loads, demand response and power conservation represent promising solutions. Despite numerous efforts, it has been shown that the current state-of-the-art fails to consider: i) the complexity of human behavior when interacting with power conservation systems; and ii) realistic home-level power dynamics. As a consequence, this leads to approaches that are i) ineffective due to poor long-term user engagement; and ii) too abstract to be used in real-world settings. In this paper, we propose an auction-theory-based power conservation framework for HVAC designed to address such individual human component through a three-fold approach: personalized preferences of power conservation, models of realistic user behavior , and realistic home-level power dynamics . In our framework, the System Operator (SO) sends Load Serving Entities (LSEs) the required power saving to tackle peak loads at the residential distribution feeder. Each LSE then prompts its users to provide bids , i.e., personalized preferences of thermostat temperature adjustments, along with corresponding financial compensations. We employ models of realistic user behavior by means of online surveys to gather user bids and evaluate user interaction with such system. Realistic home-level power dynamics are implemented by our machine-learning-based Power Saving Predictions (PSP) algorithm, calculating the individual power savings in each user’s home resulting from such bids. A machine-learning-based Power Saving Predictions (PSP) algorithm is executed by the users’ Smart Energy Management System (SEMS). PSP translates temperature adjustments into the corresponding power savings. Then, the SEMS sends bids back to the LSE, which selects the auction winners through an optimization problem called POwer Conservation Optimization (POCO). We prove that POCO is NP-hard, and thus provide two approaches to solve this problem. One approach is an optimal pseudo-polynomial algorithm called DYnamic programming Power Saving (DYPS), while the second is a heuristic polynomial-time algorithm called Greedy Ranking Allocation (GRAN). EnergyPlus, the high-fidelity and gold-standard energy simulator funded by the U.S. Department of Energy, was used to validate our experiments, as well as to collect data to train PSP. We further evaluate the results of the auctions across several scenarios, showing that, as expected, DYPS finds the optimal solution, while GRAN outperforms recent state-of-the-art approaches.

Capturing Electricity Market Dynamics in Strategic Market Participation Using Neural Network Constrained Optimization

In competitive electricity markets, the optimal bid or offer problem of a strategic agent is commonly formulated as a bi-level program and solved as a mathematical program with equilibrium constraints (MPEC). If the lower-level part of the problem can be well approximated as a convex problem, this approach leads to a global optimum. However, electricity markets are governed by non-convex (partially known) constraints and reward functions of the participating agents. In this paper, an alternative data-driven paradigm, labeled as a mathematical program with neural network constraint (MPNNC), is developed. The method uses a neural network to represent the mapping between the upper-level (agent) decisions and the lower-level (market) outcomes, i.e., it replaces the lower-level problem with a surrogate model. In the presented case studies, the proposed model is used to find the optimal load shedding strategy of a strategic load-serving entity. First, the MPNNC performance is compared to the MPEC approach, both in convex and non-convex environments, showing that the proposed MPNNC achieves similar performance to an ideal MPEC that has perfect knowledge of the simulated market environment. Then, aggregated supply curves from the Belgian spot exchange are used to assess the potential gains of using the developed model in real-life applications.

Distributed Auto-Clustering for Residential Load Profiling Using AMI Data From the U.S. High Plains

Managing demand and building customized services are essential for power retailers, distribution network operators (DNOs), and load-serving entities (LSEs) in a competitive retail environment. Therefore, understanding residential customer power demand behaviors is essential for accurate load profiling. However, the large volume of fine-grained smart meter data and the wide variety of residential behavior make load profile extraction extremely challenging, even further exacerbated by deterministic clustering’s inherent limitations. This paper develops a distributed auto-clustering approach (DAA) for load profiling using real smart meter data based on a divide-and-conquer scheme to tackle current load profiling challenges. To improve the load profiling accuracy, temperature-based categories (TBCs) are proposed to distinguish days with similar temperatures. A Heterogeneous Distributed Local Sites (HDLS) scheme is developed based on the raster map concept to distribute smart meter data among local sites while considering their size and density. Local sites send the load representatives to a central site for global clustering to significantly reduce clustering complexity and computational burden. To verify the proposed DAA, approximately 11,000 residential 15-minute smart meter data provided by a utility company from the U.S. high plains were utilized in this paper.

A Multilevel Model of Energy Market Considering Coupon Incentive Based Demand Response with Wind Power Uncertainty

Demand response (DR) can decrease or increase energy consumption and has the ability to use generating assets more efficiently. Because of the increased power consumption, it has become necessary for the power system networks either to update or upgrade the already existing power system or to inculcate some schemes to minimize the load through effective customer engagement. To address this issue incentive-based demand response proved to be an effective tool to change the pattern of consumption. This work proposes a novel multilevel energy market considering coupon incentive based DR (C-IBDR). The proposed model is operated on a modified PJM 5-Bus system where the independent service operator (ISO) using economic dispatch (ED) computes the localized marginal pricing (LMP) and the load serving entity (LSE) issues coupons to consumers and procures the demand reduction from them. Two wind generators are incorporated into the test system and their effect on the localized marginal pricing with and without demand response is analyzed. The optimization problems are modeled using particle swarm optimization (PSO) and linear programming (LP) in MATLAB. The results demonstrate that the proposed C-IBDR reduces peak demand from 900 to 860 MW through the utilization of wind and DR. This alteration modifies the load curve, increases the power factor from 0.524 to 0.555, raises the revenue of LSEs from $106,646 to $106,946, reduces generation costs from $473,291 to $214,651, and procures a total of 262 MW of demand from the consumer side in the form of demand reduction. The system is further tested for scalability. The practicality of the suggested approach in handling more extensive systems is vividly demonstrated through its application to the modified IEEE 118-bus system.

Stochastic Optimization Framework for Realizing Combined Value Streams From Customer-Side Resources–Self Service and Distribution System Operations

Customer-side resources are becoming increasingly crucial in grid modernization and transitioning to a sustainable energy future. Although most load-serving entities (LSEs) typically consider customer-side resources for single applications, these resources can be used for multiple applications simultaneously. This paper proposes a stochastic optimization framework to help LSEs capture multiple value streams from customer-side resources within their network. Specifically, we consider self-service applications – peak shaving, energy arbitrage, ramp rate reduction – and distribution system operational applications – loss reduction and voltage management. The framework is also adapted to handle the impacts of the activities of third-party aggregators on the LSE’s network. We also evaluate the performance of two algorithms – decision rule-based and optimal real-time dispatch -for dispatching the customer-side resources in the face of different sources and levels of uncertainty. Simulations were run using modified IEEE test systems within the OpenDSS simulation tool. The results show the value of customer-side resources can be maximized when multiple applications are simultaneously considered, and that value increases with increasing levels of forecast uncertainties.

Stochastic Strategic Participation of Active Distribution Networks With High-Penetration DERs in Wholesale Electricity Markets

With the increasing penetration of distributed energy resources (DERs), traditional distribution networks as load-serving entities in wholesale electricity markets, now evolve towards active distribution networks (ADNs) which can proactively participate in wholesale markets by optimally controlling the DERs in their networks. A stochastic bilevel optimization model is proposed in this paper for the strategic participation of ADNs and DERs to provide energy and grid services in wholesale electricity markets. The bilevel optimization model can capture the interactions between the ADN and the wholesale energy and ancillary service markets, considering the uncertainties of DERs in the ADN. In the upper-level model, the ADN makes optimal decisions on energy and reserve bidding considering the availability, uncertainties, and flexibility of DERs. The joint energy and reserve market-clearing of the independent system operator (ISO) is modeled as the lower-level problem. Using strong duality theory and Karush-Kuhn Tucker (KKT) conditions, the proposed bilevel optimization problem is reformulated as mathematical programming with equilibrium constraints (MPEC) problem and further converted into a computationally-solvable mixed-integer second-order-cone programming (MISOCP) model. The simulation results demonstrate the effectiveness of the model and the interactions between an ADN and wholesale electricity markets.

System and Market-Wide Impact Analysis of Coordinated Demand Response and Battery Storage Operation by a Load-Serving Entity

Because of electricity markets, environmental concerns, transmission constraints, and variable renewable energy sources (VRES), coordinated operation of demand response (DR) and battery energy storage systems (BESS) has become critical. In turn, the optimal coordinated operation of DR and BESS by an entity can affect overall electricity market outcomes and transmission network conditions. The coordinated operation is desirable for the profit-seeking entity, but it may adversely affect the cost and revenues of other market participants or cause system congestion. Though few coordinated operation models already exist, our aim in this research is to provide a novel multi-objective optimization-based methodology for the coordinated operation of DR and BESS to boost market profit. Moreover, another goal is to simultaneously study the combined effects of such coordinated models on transmission networks and electricity markets for the first time. This paper has proposed a new method for coordinated DR and BESS utilization by a load-serving entity (LSE) to increase its profit. Moreover, it has employed agent-based modeling of the electricity systems (AMES) for testing our coordinated DR and BESS method under day-ahead market and transmission system conditions. Simulation results of case studies indicate that the operating costs of all LSEs decreased, and there was as much as 98,260 $/day in cost savings for BESS deploying LSE1. Although revenues of cheaper generation companies (GenCos) decreased, those of expensive GenCos increased or showed mixed trends. For example, GenCo 3 exhibits an 8765 $/day decrease in revenue for 25% BESS capacity, whereas a 6328 $/day increase in revenue for 37.5% BESS capacity. The variance of LMPs, widely used as a risk index, greatly decreased for the LSE utilizing the coordinated methodology, somewhat decreased for other LSEs but increased for cheaper GenCos with no LSE at the local node. Since BESS deployment decisions of an LSE can have system-wide or market-wide consequences, simulation analysis before deployment can help reduce market distortions or system congestions.

Efficiency improvement of decentralized incentive-based demand response: Social welfare analysis and market mechanism design

Coupon Incentive-based Demand Response (CIDR) is a novel type of demand response mechanism targeting at small residential end-consumers, and has advantages over traditional Demand Response (DR) programs in its adaptability to most electricity retail plans, voluntary and without penalty. CIDR’s three-layer mechanism was modeled with Stackelberg Game in the authors’ previous studies, and its Nash Equilibrium is usually obtained by iterative solution. However, there is limited study on the efficiency and social welfare of the traditional CIDR mechanism. This paper provides the following contributions: (1) Introducing the response function of consumers and Load Serving Entities (LSEs) considering bounded rationality, and modeling the demand response resources as Virtual Power Plants (VPPs) to find the analytical solution; (2) By comparing the response function with centralized dispatch, this paper reveals the social welfare loss under the Nash Equilibrium of current CIDR mechanism; (3) An Extra Incentive Term (EIT) awarded to the LSE was designed to maximize the social welfare, and its form and parameters are determined under different conditions such as multiple DRs and LSEs, as well as congested power system. The case study shows that EIT has successfully helped to improve the social welfare as well as increased the retail saving and suppressed Locational Marginal Prices in most scenarios.

Incremental incentive mechanism design for diversified consumers in demand response

Demand response has been proven to be an effective way to improve energy utilization efficiency. It is notable that the diversified characteristics of residential consumers, which many greatly affect its performance in demand response, have not been fully considered in existing incentive mechanisms. In this paper, an incremental incentive mechanism for incentive-based demand response (IBDR) is proposed, in which consumers obtain different incentives according to the increment of response, so that the incentive can follow the change of consumers' marginal cost. We theoretically illustrate that the proposed incremental incentive mechanism can effectively improve the profit of load service entity (LSE), as well as the benefit of highly flexible consumers, compared with other existing incentive mechanism. In practice, LSE's bidding strategy in the day ahead market is affected by the intraday IBDR strategy that cannot be known in advance. In order to solve the bidding problem with incomplete information in the day ahead market, we propose an asynchronous double-interaction deep reinforcement learning (DRL) algorithm to maximize LSE’s cumulative profit of multiple time slots throughout the day. Numerical simulation results show that the proposed mechanism can improve the consumers' response depth while reducing the unit incentive cost, and the proposed DRL algorithm has relatively stable and satisfactory performance even in highly uncertain environment.

An optimal bidding and scheduling method for load service entities considering demand response uncertainty

With the rapid development of demand-side management technologies, load serving entities (LSEs) may offer demand response (DR) programs to improve the flexibility of power system operation. Reliable load aggregation is critical for LSEs to improve profits in electricity markets. Due to the uncertainty, the actual aggregated response of loads obtained by conventional aggregation methods can experience significant deviations from the bidding value, making it difficult for LSEs to develop an optimal bidding and scheduling strategy. In this paper, a bi-level scheduling model is proposed to maximize the net revenue of the LSE from optimal DR bidding and energy storage systems ESS scheduling by considering the impacts of the uncertainty of demand response. An online learning method is adopted to improve aggregation reliability. Additionally, the net profit for LSEs can be raised by strategically switching ESS between two modes, namely, energy arbitrage and deviation mitigation. With Karush–Kuhn–Tucker (KKT) optimality condition-based decoupling and piecewise linearization applied, this bi-level optimization model can be reformulated and converted into a mixed-integer linear programming (MILP) problem. The effectiveness and advantages of the proposed method are verified in a modified IEEE RTS-24 bus system.

Managing power imbalance with cloud energy storage in imbalance band market environment

Imbalance band market is an alternative to traditional market structures which is proposed to ensure that system frequency remains within acceptable bounds. In an imbalance band market environment, the load-serving entities (LSEs) can purchase bands or reduce the load deviation by operating a battery energy storage system (BESS) to avoid the penalty for causing imbalance. The market concept would be effective in sharing balancing responsibility between a system operator (SO) and load-serving entities. In this study, the authors proposed a planning framework for a load serving entity (LSE) considering cloud energy storage (CES) business as a useful resource in the imbalance band market environment. The term of cloud energy storage is used as a platform that the operator owns and operates the storage, while subscribed clients pay a service fee for requesting charging and discharging operations. The simulation results show that operating cloud energy storage business is economically rational strategy for the LSE. The authors verified that a LSE operating the cloud energy storage business under an imbalance band market environment to pursue its own benefit better performs a part of the balance-maintaining obligation of the SO compared with a LSE not operating the cloud energy storage business.

Demand response considering user behaviour differences for load serving entity: A multi‐agent deep reinforcement learning approach

Demand response can reshape the load profiles by adjusting the energy price to improve overall performance of power grids. For the load serving entities (LSEs), the optimal demand response shall satisfy operation constraints and gain maximised profits. However, the individual end-consumer will surely value electricity differently, and price adjustments without considering the differences of user behaviours may induce unbalanced dispatch and degrade the overall profits. Given affordable price ranges under which the non-critical loads can properly response to price signals, the demand response can be operated in a manner where non-critical loads at different areas coordinately adjusted to achieve the global dispatch objectives. For this purposes, this paper proposes a cooperative demand response approach for LSE pricing based on a multi-agent deep reinforcement learning approach. The learning approach constructs the distributed pricing agents that cooperatively determine the price signals to be responded by different load clusters. To update the parameters of pricing agents, the deep deterministic policy gradient is derived considering the constraints and different behaviours of load clusters. The effectiveness of the proposed cooperative demand response method is verified based on numerical simulations.

A Stochastic Optimization Framework for Realizing Combined Value Streams From Customer-Side Resources

Due to numerous supporting policies aimed at decarbonizing electricity infrastructures in different regions of the world, customer-side resources are becoming increasingly valuable. Consequently, load serving entities (LSEs) which typically have access to these customer-side resources can use them for multiple services simultaneously. In this paper, we discuss a stochastic optimization framework for using clusters of residential HVACs, electric water heaters (EWH) and behind-the-meter (BTM) batteries, spread around the LSE’s distribution network, for energy arbitrage, peak shaving and market based frequency regulation simultaneously. Our framework captures the effects of controlling the consumption of the customer-side resources on the voltages in the LSE’s distribution network. We also discuss two real-time dispatch algorithms capable of eliciting fast response from the resources to derive regulation signals from the market operator with minimal voltage violations. We evaluate the optimization models and dispatch algorithms using a HELICS-based co-simulation platform and real-world data from New York Independent System Operator (NYISO).

Influence of business models on PV-battery dispatch decisions and market value: A pilot study of operating plants

PV-battery hybrid projects dominate interconnection queues in some regions in the United States. But few large-scale projects have been in use long enough to assess how the hybrid capabilities may be used in practice and the existing literature scantly discusses observed operational strategies. We interview plant operators and analyze empirical dispatch data for eleven large-scale PV-battery hybrids in three organized wholesale markets in the United States. We estimate the market value of our sample hybrids in 2020. The empirical increase in market value of a PV-battery hybrid relative to a standalone PV plant varies by project and ranges from $1 to $48/MWhsolar, often aided by a large boost in capacity value. This premium is driven by market, location, technical characteristics of the PV and battery asset, and battery dispatch strategies. In contrast to the widespread assumptions in the PV-battery hybrid modeling literature, only three of the eleven project operators optimize battery usage for wholesale market revenue as merchant plants. Instead, load-serving entities target peak load reductions, incentive program participants focus on compliance with program requirements, and large energy consumers prioritize resiliency and utility bill minimization. These alternative business models can result in high revenues for the project operators, but do not optimize the storage dispatch from a grid perspective. Understanding real-world dispatch signals and aligning them closer with system-wide grid needs will be important for electric grid operators and system planners, and can increase the market value of PV-battery hybrids.

An Online Learning Framework for Targeting Demand Response Customers

We study the optimal design of demand response and peak-time rebate programs in the electricity sector. Load-serving entities (LSEs) frequently use demand response or peak-time rebate programs to reduce consumption during peak hours. However, LSEs have imperfect information, so they typically estimate a customer’s counterfactual energy consumption—often referred to as ‘baseline’— and offer compensations for reductions below this threshold. Due to the inherent variability and uncertainty of demand, this approach may lead to generous incentives to customers who make no effort to reduce their demand, and underpayment to those who do. We show that the estimated baseline is not necessarily the optimal threshold for calculating rebates in demand response programs. The LSE needs to learn about customers’ demand patterns and also customers’ propensity for reductions over time. We bring in tools and methodologies from online learning to formulate and solve the LSE’s optimization problem in designing demand response program. By appropriately tuning the parameters, the LSE can use this framework to balance multiple objectives to determine the appropriate rebate threshold for customers. Using the data from a ToU pricing experiment in London, we show that compared to a conventional demand response program, our approach significantly improves the targeting of demand response payments.

Bi-Level Approach for Load Serving Entity’s Sale Price Determination under Spot Price Uncertainty and Renewable Availability

Load serving entities (LSEs) maximise their profit by increasing the difference between revenue from electricity sale to consumers and the cost of procuring that electricity. Considering the offered sale prices, consumers minimise energy bills by scheduling their energy consumption. Varying spot market prices and consumer behaviour impact LSE’s electricity procurement and sale price decisions. The two conflicting objectives of LSE profit maximization and consumer cost minimization can be modelled effectively by hierarchical bi-level programming. Additionally, LSE has to consider spot market price uncertainty and renewable availability during different hours of the day. This paper considers these issues for LSE’s risk-based profit maximization decision making model under RE availability by proposing a bi-level framework to determine optimal dynamic sale prices and energy procurement decisions. The upper level considers risk-based profit maximization for LSE and the lower level addresses the consumer’s objective of cost minimization. This work considers Conditional Value at Risk (CVaR) to model spot market price risk for pragmatic characterisation of LSE’s risk-averse behaviour. A case study on the PJM market shows the effectiveness of the proposed approach.

Dynamic sale prices for load serving entity's risk based profit maximization

In any retail market, Load Serving Entities (LSE) and consumers can benefit from an effective dynamic pricing mechanism. Consumer's price responsiveness and uncertainty of Wholesale Electricity Market (WEM) prices can offer unexpected profits for LSE. LSEs are incentivised to procure a portion of their energy demand from Energy Storage Systems (ESS) due to increasing renewable penetration at the distribution end. LSE's decisions become complex while handling uncertainty arising from such constantly varying conditions. Sale price dynamics optimization could minimize undesirable impact of these uncertainties and maximize LSE's profit. However, this optimization is subject to severe WEM price uncertainty. In this perspective, this paper proposes a sale price dynamics optimization model to maximize LSE's profit while immunizing LSE against severe uncertainty of WEM prices. Information Gap Decision Theory (IGDT) framework is considered to explicitly model severe price uncertainty and determine robust and opportunistic decisions. Impact of Renewable Energy (RE) availability along with Battery Energy Storage (BES) operations on procurement decisions is analyzed. Results highlight that the proposed model ensures consumer benefit and increases LSE's profit. Correlation variation between demand and WEM price profile establishes effectiveness of the proposed model. RE and BES enhance robustness of procurement decisions.

A Peer-to-Peer Energy Trading Framework for Wind Power Producers With Load Serving Entities in Retailing Layer

This article proposes a peer-to-peer (P2P) energy trading framework for wind power producer (WPP) in the retailing layer to increase its revenue and to promote wind power utilization. In this framework, the WPP can provide energy consumption of demand response providers (DRPs) in a competitive environment through both the main grid and rival load-serving entities. Also, based on a P2P pricing mechanism, the WPP can choose if to trade energy in a P2P platform or not. The proposed problem is formulated as a stochastic bilevel optimization model, in which in the upper level, the WPP profit is maximized and in the lower level, the DRPs participate in DR programs and tend to choose the fairest supplier among the WPP and LSEs to minimize their energy procurement costs. The proposed method is applied to a realistic case study and the results demonstrate that with P2P, the WPP can schedule the energy transaction with peers to offset part of the energy deviations. Moreover, different values of P2P prices lead to different values of energy transactions due to the relatively diversity pattern of local generation of rival LSEs and their offering prices and demand and generation of the WPP.

Business Feasibility Study for Storage-Based Customer Flexibility Platform of Load-Serving Entity

A storage-based customer flexibility (SCF) platform enables on-demand remote access to a shared pool of utility-scale energy storage resources at substantially low costs. It can provide users the ability to charge and discharge electrical energy to and from remote batteries. This study demonstrates the economic feasibility of an SCF platform business under realistic conditions. Accordingly, the load-serving entity is set as the SCF operator, and the subscriptions from various types of customers and multi-use battery energy storage systems are considered to increase profitability. An economic feasibility verification is provided based on the generating pattern of individual customers, and a robust optimization is performed for the SCF platform operations considering the market price uncertainty, aggregated requests, and customers' willingness to pay for the SCF service. The proposed study is simulated in a California environment.