Load Serving Entities Research Articles

A dynamic decision-making method for energy transaction price of CCHP microgrids considering multiple uncertainties

Combined cooling, heating and power microgrid (CCHP microgrid) system, which has the characteristics of clean production and multi-energy comprehensive supply, has attracted great attention. Through the integration of renewable energy, etc., the electricity retailer can form a load service entity (LSE) to provide services. Transactions can be made between CCHP microgrid and LSE. However, how to determine the transaction price between them is still a problem. In addition, both the CCHP microgrid and LSE contain renewable energy with uncertain output, which affects the normal operation of the system. In this paper, we propose a dynamic decision-making method for the energy transaction price considering multiple uncertainties. According to the random output of renewable energy, the uncertainty set is constructed. Furthermore, a robust optimal dispatching model is established. According to the dispatching results, the optimal transaction price is searched by using the salp swarm algorithm (SSA). It can be seen from the simulation results: 1) This price decision method can effectively reduce the operating cost of the system; 2) The LSE acts as an energy exchange center in the entire system; 3) Using robust optimization for energy dispatching can maximize the consumption of renewable energy.

Real-Time Algorithm Based Intelligent EV Parking Lot Charging Management Strategy Providing PLL Type Demand Response Program

Thanks to the demand response (DR), the system operators have a substantial opportunity to manipulate the demand side of the power system. Moreover, the system operators can easily incorporate trending technologies such as electric vehicles (EV) due to the idea of introducing improved level of controllable loads. In this study, a real-time optimization-based energy management model for an EV parking lot (EVPL) is propounded by using linear programming. The proposed algorithm offers a peak load limitation oriented DR program providing operational flexibility from the load-serving entity point of view together with the objective of maximizing the load factor of the EVPL in daily operation. Because of the mobility of the EVs, uncertain arrival/departure times along with the state-of-energy levels upon their arrival are generated by considering historical data to ensure a more realistic approach. In order to prove the validness of the suggested optimization model, a bunch of case studies is performed. Besides, credible results and useful findings are found out by activating the propounded model.

Stochastic multi-objective optimization to design optimal transactive pricing for dynamic demand response programs: A bi-level fuzzy approach

This paper proposes a stochastic multi-objective optimization framework to design real-time pricing for transactive energy. The energy transaction among the load-serving entity and customers is formulated as a bi-level optimization in which load-serving entity acts as the leader in the upper-level of optimization aiming to profit and reserve maximization. To make the interaction between opposite objectives, the modified fuzzy method is employed. The load-serving entity takes part in the wholesale market and purchases energy from the wholesale market and sells it to the customers. The load-serving entity itself possesses some dispatchable resources and it can also buy energy from privately-owned renewable resources. On the other hand, demand response providers are buying energy from the load-serving entity on behalf of consumers. The demand response providers are the followers of the proposed model and seek to maximize the profit of their customers. The communication between LSE and customers can be provided by the energy internet. Using the energy internet, the LSE controls and monitors the behavior of costumers to design real-time prices. The proposed model is tested on a standard case study, and the results show that the reservation and energy not supplied have been improved as 12% and 43.8%, respectively.

An ERCOT test system for market design studies

An open source test system is developed that permits the dynamic modeling of centrally-managed wholesale power markets operating over high-voltage transmission grids. In default mode, the test system models basic operations in the Electric Reliability Council of Texas (ERCOT): namely, centrally-managed day-ahead and real-time markets operating over successive days, with congestion handled by locational marginal pricing. These basic operational features characterize all seven U.S. energy regions organized as centrally-managed wholesale power markets. Modeled participants include dispatchable generators, load-serving entities, and non-dispatchable generation such as unfirmed wind and solar power. Users can configure a broad variety of parameters to study basic market and grid features under alternative system conditions. Users can also easily extend the test system’s Java/Python software classes to study modified or newly envisioned market and grid features. Finally, the test system is integrated with a high-level simulation framework that permits it to function as a software component within larger systems, such as multi-country systems or integrated transmission and distribution systems. Detailed test cases with 8-bus and 200-bus transmission grids are reported to illustrate these test system capabilities.

Optimal Coordination Strategies for Load Service Entity and Community Energy Systems Based on Centralized and Decentralized Approaches

The energy interaction among a load service entity and community energy systems in neighboring communities leads to a complex energy generation, storage, and transaction problem. A load service entity is formed by a local electricity generation system, storage system, and renewable energy resources, which can provide ancillary services to customers and the utility grid. This paper proposes two coordination schemes for the interaction of community-based energy systems and load service entities based on game-theoretic frameworks. The first one is a centralized coordination scheme with full cooperation, in which the load service entity and community energy systems jointly activate the local resources. The second one is set as a decentralized coordination scheme to obtain a relative balance of interests among the market participants in a Stackelberg framework. Two mathematical models are developed for the day-ahead decision-making of the above energy management schemes. The Shapley value method, Karush-Kuhn-Tucker conditions, and strong dual theory are applied to solve the complex coordination problems. Numerical study shows the effectiveness of the coordination strategies that all stakeholders benefit from the proposed coordination schemes and create a win–win situation. In addition, sensitivity analysis is conducted to study the effects of system configuration, energy demand, and energy prices on the economic performance of all stakeholders. The results can serve as references for business managers of the load service entity.

The use of wholesale market purchases by U.S. electric utilities

Load Serving Entities (LSEs) can procure electricity from their owned generation or purchase it on bilateral or organized markets. Electric utility procurement decisions have important implications for rates, resource adequacy, environmental impacts, and LSE cost recovery. However, there is little or no research on trends of utility procurement practices—or the role of wholesale market transactions. This paper leverages information from the U.S. Department of Energy's Energy Information Administration, Berkeley Lab's Resource Planning Portal, and structured interviews with regulatory and utility staff. We find that use of market transactions to meet electric energy needs has been increasing across U.S. LSEs. The reasons for this trend include a general stagnation in building owned resources, a preference towards contracting renewable energy resources to meet RPS targets, and the lower relative cost of wholesale market electricity. Results suggest resource adequacy obligations may drive preferences for asset ownership more than a profit incentive. We also recommend improving the planning to procurement connection by incorporating operational constraints of market transactions within planning processes. These results provide insights on key issues in electric utility procurement that could improve overall market efficiency and identify practices that stakeholders should consider incorporating into their long-term capacity planning processes.

Online Residential Demand Response via Contextual Multi-Armed Bandits

Residential loads have great potential to enhance the efficiency and reliability of electricity systems via demand response (DR) programs. One major challenge in residential DR is how to learn and handle unknown and uncertain customer behaviors. In this letter, we consider the residential DR problem where the load service entity (LSE) aims to select an optimal subset of customers to optimize some DR performance, such as maximizing the expected load reduction with a financial budget or minimizing the expected squared deviation from a target reduction level. To learn the uncertain customer behaviors influenced by various time-varying environmental factors, we formulate the residential DR as a contextual multi-armed bandit (MAB) problem, and develop an online learning and selection (OLS) algorithm based on Thompson sampling to solve it. This algorithm takes the contextual information into consideration and is applicable to complicated DR settings. Numerical simulations are performed to demonstrate the learning effectiveness of the proposed algorithm.

Bilateral negotiations for electricity market by adaptive agent-tracking strategy

Bilateral transactions hedge both sides against uncertain price and volume risks of day-ahead auction and make up major portions of trading in electricity markets. Peer-to-peer bilateral transactions avoid broker fees but involve challenges of balancing between cooperative and competitive strategies for multi-round negotiations. To solve these challenges, this paper develops novel utility-based and adaptive agent-tracking strategies for bilateral negotiations. Relying on bilateral transaction volume and utility curves determined over a price range during unilateral pre-negotiation, utility-based strategies are developed for generation company (GenCo) agent (load serving entity (LSE) agent) to offer (bid) volumes and prices during multi-round bilateral negotiations. GenCo agent is also equipped with a new adaptive agent-tracking strategy that estimates reservation price of each LSE agent by Bayesian learning and updates the estimates in each round. The adaptive agent-tracking strategy facilitates cooperative yet competitive responses. Integration of new bilateral negotiation strategies with existing day-ahead auction in a renowned agent-based platform also enables combined simulation of the two market types. The case study demonstrates that the adaptive agent-tracking strategy empowers GenCoagents to swing bilateral negotiation results in their favor and yield 7% more payoff than the utility-based strategy, while achieving 100% improvement in frequency of failure of negotiation.

Stochastic Optimal Energy Management and Pricing for Load Serving Entity With Aggregated TCLs of Smart Buildings: A Stackelberg Game Approach

With the development of demand-side management in the smart grid, load-serving entity (LSE) plays a more important role for consumers, which purchases energy from the electricity market and sells it to consumers. Moreover, aggregated thermostatically controlled loads (TCLs) in smart buildings can provide additional demand response capacities and require efficient energy management methods. This article proposes a stochastic optimal energy management and pricing model for LSE with aggregated TCLs and energy storage based on the Stackelberg game and stochastic programming. The energy management and pricing problem are formulated as a bilevel optimization model. The upper level model aims to maximize LSE's expected profit under market price uncertainties and determines the offering prices to consumers. According to the offering price from upper level model, the lower level model optimizes the power purchasing pattern for consumers of two types of buildings with TCLs: factory and office buildings. The nonlinear bilevel model is reformulated and converted into mixed-integer linear programming using a strong duality theory. The proposed model is validated by numerical studies based on real market prices from the PJM electricity market. In addition, the impacts of energy storage, number of buildings, comfortable indoor temperature limits, and offering price limits on LSE's profit are analyzed.

Prediction and assessment of demand response potential with coupon incentives in highly renewable power systems

Demand Response (DR) provides both operational and financial benefits to a variety of stakeholders in the power system. For example, in the deregulated market operated by the Electric Reliability Council of Texas (ERCOT), load serving entities (LSEs) usually purchase electricity from the wholesale market (either in day-ahead or real-time market) and sign fixed retail price contracts with their end-consumers. Therefore, incentivizing end-consumers’ load shift from peak to off-peak hours could benefit the LSE in terms of reducing its purchase of electricity under high prices from the real-time market. As the first-of-its-kind implementation of Coupon Incentive-based Demand Response (CIDR), the EnergyCoupon project provides end-consumers with dynamic time-of-use DR event announcements, individualized load reduction targets with EnergyCoupons as the incentive for meeting these targets, as well as periodic lotteries using these coupons as lottery tickets for winning dollar-value gifts. A number of methodologies are developed for this special type of DR program including price/baseline prediction, individualized target setting and a lottery mechanism. This paper summarizes the methodologies, design, critical findings, as well as the potential generalization of such an experiment. Comparison of the EnergyCoupon with a conventional Time-of-Use (TOU) price-based DR program is also conducted. Experimental results in the year 2017 show that by combining dynamic coupon offers with periodic lotteries, the effective cost for demand response providers in EnergyCoupon can be substantially reduced, while achieving a similar level of demand reduction as conventional DR programs.

A Stochastic Bilevel Model for an Electricity Retailer in a Liberalized Distributed Renewable Energy Market

This article presents a short-term decision-making model for an electricity retailer using bilevel stochastic programing. In the proposed model, a liberalized distributed renewable energy (DRE) market in which the retailer competes with other load serving entities (LSEs) for procuring DRE is proposed. The retailer, in the upper level, decides its level of involvement in the day-ahead and real-time markets, as well as the price bids offered to DRE producers for every time period, with the goal of minimizing its expected procurement cost at a predefined risk level. On the other hand, DRE producers, in the lower level, react to the price bids offered by the retailer under study and other LSEs, to maximize their total revenues. The stochastic nature of day-ahead and real-time market prices, DRE production, electricity demand, and price bids of the retailer's rival market agents (RMAs) is taken into the formulation of the proposed model. By using the Karush-Kuhn-Tucker (KKT) optimality conditions and duality theory, the bilevel problem is transformed into its equivalent single-level mixed-integer linear programming (MILP) problem. Case studies are performed to show the effectiveness of the proposed model.

Research on real-time pricing strategy for load serving entity based on simultaneous perturbation stochastic approximation

As one of the main participant in the electricity market, load serving entities (LSE) purchase electricity in the wholesale market, and provide different types of electricity package to users in the retail market. LSE’s profit mainly comes from the difference between the cost of purchasing electricity and the benefit of selling electricity. To reduce the cost, LSE can optimize electricity consumption characteristics of various types of users through real-time price adjustment, which can help to improve the shape of electricity purchase curve and implement demand side response. In this paper, a real-time pricing strategy based on synchronous perturbation stochastic approximation algorithm (SPSA) is proposed. This method considers the difference between diverse users synthetically. Depending on the historical electricity consumption and historical price data, the user side response function is constructed, and the real-time electricity price is formulated with the objective of minimizing load curve’s peak-valley difference. Finally, the effectiveness and advantages of the proposed method are verified by comparing the load curve and the power purchase cost before and after the price adjustment.

Neural-network-based Lagrange multiplier selection for distributed demand response in smart grid

As a general difficult problem, the slow convergence of existing distributed demand response methods greatly hinders the reliable applications in smart grid. To overcome this problem, this paper proposes a new distributed method, namely the neural-network-based Lagrange multiplier selection (NN-LMS), to prominently reduce the iterations and avoid an oscillation. The key improvement lies in the forecast strategy of a load serving entity (LSE), who applies a specially designed neural network to capture the users’ price response features. A novel Lagrange multiplier selection model, the NN-LMS model, is formulated to optimize the iterative step sizes. This complex model is then solved by a two-stage NN-LMS algorithm, containing the interval bisection method and improved sensitivity method in the first and second stages, respectively. In addition, the data selection, batch normalization and additional network are applied to boost the performance of neural networks. Case studies validate the optimality, improved convergence and numerical stability of the proposed method, and demonstrate its great potential in distributed demand response and other smart grid applications.

Differentiated Incentive Strategy for Demand Response in Electric Market Considering the Difference in User Response Flexibility

In demand response programs, load service entity (LSE) can aggregate user as an independent entity to participate in the day ahead energy market, and complete the response target through incentive in the next day. In order to reduce the incentive cost of LSE, a differentiated incentive mechanism that consider the differences in user response flexibility is proposed in this paper. Then, a user response behavior model is established by using the long short-term memory (LSTM) network, with aim of accurately predicting users' response. Subsequently, an optimization strategy combining particle swarm (PSO) and LSTM is proposed, so that the response target can be accurately completed with low cost. Simulation experiments verified that the cost of LSE is close to the theoretical minimum, and can be reduced by 20% compared with the optimal result under unified incentive mechanism. Moreover, it also verified that the proposed strategy has high response accuracy and good stability.

Evaluating the market power and coalition formation in double-sided power market: An analytical decomposition approach

In this paper, new indices of the market power are defined to recognize and prioritize the potentials of market power as well as the feasibility of forming a profitable coalition of electricity market players. To this aim, the social welfare optimization problem is formulated and solved in a double-sided power market environment. Then, by forming the Lagrange function, Karush Kuhn Tucker (KKT) conditions are analytically developed. Three major outputs of the above problem including the Locational Marginal Price (LMP), the generation power of each unit and the dispatched elastic load of each Load Service Entity (LSE) are decomposed into basic structural factors. These factors include supply, demand and line congestion effects. Using the coefficients obtained from the structural decomposition, four profit-based market power indices are formulated: (1) profit index of each generator, or GenCo, due to the change of generation units’ bids; (2) profit index of each generator, or GenCo, due to the change of LSEs’ offers; (3) benefit index of each LSE due to the change of generation units’ bids, and (4) benefit index of each LSE due to the change of their offers. The impact of changes in a player’s bidding/offering strategy on its own profits determines the market power of that player. In addition, these changes affect other players' profits. So, the possibility of forming a profitable coalition can be evaluated. The proposed method is simulated on the IEEE 24-bus test system and the proposed indices are calculated and analyzed. Assessment results provide valuable implications for market regulators to monitor the competition level of the power market.

Deep Reinforcement Learning for Joint Bidding and Pricing of Load Serving Entity

In this paper, we address the problem of jointly determining the energy bid submitted to the wholesale electricity market (WEM) and the energy price charged in the retailed electricity market (REM) for a load serving entity (LSE). The joint bidding and pricing problem is formulated as a Markov decision process (MDP) with continuous state and action spaces in which the energy bid and the energy price are two actions that share a common objective. We apply the deep deterministic policy gradient (DDPG) algorithm to solve this MDP for the optimal bidding and pricing policies. Yet, the DDPG algorithm typically requires a significant number of state transition samples, which are costly in this application. To this end, we apply neural networks to learn dynamical bid and price response functions from historical data to model the WEM and the collective behavior of the end use customers (EUCs), respectively. These response functions explicitly capture the inter-temporal correlations of the WEM clearing results and the EUC responses and can be utilized to generate state transition samples without any cost. More importantly, the response functions also inform the choice of states in the MDP formulation. Numerical simulations illustrated the effectiveness of the proposed methodology.

Economic Operation of Thermostatic Loads Under Time Varying Prices: An Optimal Control Approach

In this paper, we consider the problem of optimal precooling of thermostatic loads by a load serving entity (LSE) under time varying electricity prices. Specifically, we propose a low complexity dynamic programming based algorithm for controlling the switching of heating, ventilation, and cooling (HVAC) units such that the cost of cooling is minimized while ensuring that the resultant temperatures do not violate the comfort constraints of consumers. Specifically, our algorithm makes use of two facts: first, the price of electricity vary discretely in time; and second, the cost-optimal state trajectory under fixed electricity price and ambient temperature, with given initial and final states, can be characterized explicitly in closed-form. We validate our algorithm with a set of numerical simulation studies using real market prices of electricity from the New York and Texas electricity markets. In addition, we also provide an approximation mechanism to implement this optimization algorithm in HVAC systems that have ON-OFF switching constraints.

Learning-Based Demand Response for Privacy-Preserving Users

Demand response (DR), as a vital component of smart grid, plays an important role in shaping the load profiles in order to improve system reliability and efficiency. Incentive-based DR has been used in many DR programs by incentivizing customers to adapt their loads to supply availability. Note that users’ behavior patterns can be easily identified from fine-grained power consumption when interacting with the load serving entity (LSE), giving rise to serious privacy concerns. One common approach to address the privacy threats is to incorporate perturbations in users’ load measurements. Although it can protect the users’ privacy, yet the usage data modification would degrade the LSE's performance in achieving an optimal incentive strategy due to unknown characteristics of the augmented perturbations. In this paper, we cast the incentive-based DR problem as a stochastic Stackelberg game. To tackle the challenge induced by users’ privacy protection behaviors, we propose a two-timescale reinforcement learning algorithm to learn the optimal incentive strategy under users’ perturbed responses. The proposed algorithm computes the expected utility cost to mitigate the impacts of the random characteristics of the augmented perturbations and then updates the incentive strategy based on the perceived expected utility costs. We derive the conditions under which the proposed incentive scheme converges almost surely to an $\epsilon$ -optimal strategy. The efficacy of the proposed algorithm is demonstrated using extensive numerical simulation using real data.

Multi‐objective bi‐level optimisation to design real‐time pricing for demand response programs in retail markets

Electric energy consumption is progressively increased requiring new investments in the network to meet. Demand response programs (DRPs) are of practical tools to defer and/or decrease these investments. In this study, a real-time DRP is proposed to promote consumer participation in the energy delivery. The proposed DRP is designed on behalf of the Load Serving Entity (LSE) and aims to maximise its profit. The LSE purchases energy from the up-stream wholesale market and offers to the consumers in the retail market. The consumers, with the objective of maximising the profit, have the choice to purchase energy from the retail market or the LSE. The problem is formulated as a mixed integer linear bi-level programming model wherein LSE and aggregators, on behalf of the consumers, act as the leader and followers of the problem. Also, to avoid creating a new peak profile in light load periods promoted by the designed LSE prices, the upper level problem is tailored as a multi-objective formulation wherein flattening load profile in addition to the maximising LSE profit is considered. The proposed model is implemented on a test case and the results show that the both LSE and consumers benefit is increased with a smoother load profile.

What should a small load serving entity use to hedge its procurement cost risk?

This paper answers the question: what should a small load serving entity (LSE) use to hedge its procurement cost risk? In doing so, it proposes a transparent process to compute a hedging strategy’s procurement cost expectations and ceilings. Its key finding is that if the LSE is risk averse, it should use electricity forwards when the forward price premium is relatively small and cross hedging with natural gas futures is relatively ineffective.