Load Serving Entities Research Articles

Game-based strategy for load service entity to level Duck Chart as well as gain EV consumers

Summary With the increasing penetration of unregulated solar energy, a load service entity (LSE) can exert an externality cost to guide the charging behaviors of electric vehicle (EV) users and level the Duck Chart. This paper introduces a dynamic Stackelberg game model between LSE and EV users to analyze consumer behavior, and a complete information static game model to help LSEs increase profits by gaining share of the EV market. These dynamic and static game models are incorporated into a novel theoretic algorithm with mixed integer programming. A case study shows that the models and the proposed algorithm can level the Duck Chart and guarantee an LSE's share of the EV market. Copyright © 2016 John Wiley & Sons, Ltd.

Risk Constrained Scheduling of Energy Storage for Load Serving Entities Considering Load and LMP Uncertainties

Abstract: With the substantial technique development, energy storage (ES) is becoming a promising resource to improve the flexibility and reliability of power system operation. Consequently, ES is obtaining increasing attention from both academia and industry. Due to the increasing fluctuation of locational marginal prices (LMP) as a result of the high penetration of variable wind generation, obtaining the deterministic scheduling decision for ES, i.e., charging/discharging, becomes more complex for load serving entities (LSEs). To address this challenge, a risk constrained scheduling model is proposed in which the objective is to maximize the LSE’s profit by optimially scheduling ES charging/discharging profile and considering the possible financial loss risk. Conditional value at risk (CVaR) term has been included in the objective function to negate the risk of associating the uncertainties from forecasting the market price and load. Numerical examples verify the proposed method in ES scheduling considering risk.

An 8-Zone Test System Based on ISO New England Data: Development and Application

This study develops an open-source 8-zone test system for teaching, training, and research purposes that is based on ISO New England structural attributes and data. The test system models an ISO-managed wholesale power market populated by a mix of generating companies and load-serving entities that operates through time over an 8-zone AC transmission grid. The modular extensible architecture of the test system permits a wide range of sensitivity studies to be conducted. To illustrate the capabilities of the test system, we report energy cost-savings outcomes for a comparative study of stochastic versus deterministic DAM security constrained unit commitment (SCUC) formulations under systematically varied reserve requirement levels for the deterministic formulation.

Sequential Contracts for Uncertain Electricity Resources

The U.S. department of energy recently listed “development of rules for market evolution that enable system flexibility” among the key strategic areas of intervention for a successful integration of renewable energy into the grid [2]. Renewable energy generation from wind and solar is intermittent and its prediction accuracy is precise only within a short time horizon (e.g. 5-15 minutes [1]). Therefore, new market mechanism solutions such as intra-day markets are proposed that allow for flexible generation of renewable energy to exploit the improved forecast accuracy of renewable resources over time [14]. In this paper we study sequential contract design problems that incorporate the arrival of new information about renewable generation and allow for flexible production. Today, the renewable energy generation receives extramarket treatment such as feed-in tariffs, guaranteed grid access, and lenient penalty rate [3, 7]. For example, the Participating Intermittent Resource Program (PIRP) mandates the California independent system operator to accept all the wind generation in real-time and treat them as negative loads. The subsequent increased cost of the required reserve generation capacity is then socialized among the load serving entities (LSE). However, such approaches cannot be sustained for high levels of renewable generation as the imposed reserve generation cost on LSEs becomes excessively high and results in social welfare loss. In the long-run, renewable energy generation needs to participate in electricity markets and be exposed to market mechanisms. An alternative approach, implemented in the U.K., requires the wind generators to bid in conventional electricity markets and pay penalty for ex-post deviation from their ex-ante contracted schedule. Such a firm contracting approach is the subject of many studies in the literature. The works of [4, 15] study the problem of optimal bidding in a two-settlement market structure with an exogenous price and penalty rate. The problem of mechanism design for wind aggregation among many wind producers that jointly participate in a two-settlement market structure with exogenous price and penalty rate is investigated in [8, 10]. The authors in [12] study the problem of auction design for such a two-settlement market structure. In this paper, we propose a simple two-step model to capture the dynamic variable nature of renewable generation and provide a general formulation for flexible forward con-

Ranking Index in the Pareto Optimal Front Based Evolutionary Programming Technique for Best Selection of Several Optimal Multi-Objective Capacity Benefit Margins

The amount of power transfer between areas is purporting as imperative information required by the utility so that this will assist them towards an effective operation of electricity market performed in a deregulated power system. CBM is defined as the amount of the transfer capability reserved by the load-serving entities which will be used during the case of generation deficiency. This paper presents a new approach used to perform simultaneous determination of capacity benefit margin (CBM) for all areas by using the evolutionary programming (EP) technique. Ranking index in the Pareto optimal front cluster of total loss-of-load expectation (LOLE) and total LOLE difference will be used for selecting several best solutions of multi-objective CBMs. Eventually, performance of the proposed method is investigated thoroughly via a test system of modified IEEE-RTS79. Utilization of the proposed technique is superior in terms of offering flexibility to the utility in selecting several best solutions of multi-objective CBMs.

A Nine Zone Electric Power Market Test System Based on Data from the Midcontinent Independent System Operator

This study develops a 9-zone test system based on the Midcontinent Independent System Operator (MISO) data for application in electric power market studies. This 9-zone test system models MISO's original seven midwestern zones together with two recently-incorporated southern zones operating over a 15-line AC transmission grid. Generators with different fuel types and capacities submit hourly supply offers in a day-ahead market (DAM). Load-serving entities submit hourly demand bids into a day-ahead market in the form of a 24-hour regional load profile. This 9-zone test system allows a wide range of sensitivity studies. To illustrate the capabilities of the 9-zone test system, this study undertakes a comparative study of DAM Locational Marginal Price (LMP) outcomes for MISO prior to and after the integration of the two southern zones by conducting test-bed simulations for 7-zone and 9-zone test cases based on MISO data.

Assessing the financial impacts of distributed energy on load serving entities

This article analyzes the financial impact of distributed energy resources (DERs) owned and operated by commercial customers on the load serving entities (LSEs). DERs reduce the customers' electricity bills and hence the revenues collected by their LSE. However, changes in customer demand profiles can potentially reduce the aggregated system demand profile, and therefore, reduce the LSE's costs in wholesale markets. Analysis of these financial impacts indicates that the LSE's lost revenue ultimately outweighs its reduced expenses. This is largely due to a significant reduction in revenue from demand charges. Dispatchable DERs, including energy storages and demand response, result in more financial losses for LSEs than photovoltaics. The financial losses LSEs face indicate that redesigning commercial customer tariffs is necessary in order for LSEs to accommodate customer owned DERs properly. Several suggestions on modifying commercial tariffs are presented.

HARDWARE PROTOTYPE OF SMART HOME ENERGY MANAGEMENT SYSTEM

The development of ‘demand-side load management’ is the outcome of the smart grid initiative. Due to the significant amount of loads in the residential sector, home energy management has received increasing interest. In the country like India, we are lagging behind, in the power sector as the demand is much more than the supply. Moreover, there is not a single initiative, which has been taken for the deployment of smart suppliers and smart users. Here, I propose a hardware design of smart home energy management system (SHEMS). With the help of this proposed design, it is possible to have a real-time, price-responsive control strategy for domestic loads such as electrical water heater (EWH), illumination (Lights), air conditioning (Fan), dryer etc. Consumers may interact with suppliers or load serving entities (LSEs) to facilitate the load management at the supplier side. This system is designed with sensors to detect human activities and the behavior is predicted by applying a machine learning algorithm in order to help consumers reduce total payment on electricity. Finally, for the verification of the hardware system, simulation and experiment results will be checked based on an actual SHEMS prototype.

Allocating the Cost of Demand Response Compensation in Wholesale Energy Markets

Currently, demand response resources can sell load reductions in wholesale energy markets. However, paying for load reductions ultimately results in an unbalanced market, where the amount of resources sold (megawatts) is less than the amount of resources bought (megawatts and “negawatts”). To resolve this imbalance, the ISO must allocate the cost of compensating demand response to those buyers who benefit from reduced LMPs. Current cost allocation methods are quite broad and based on each energy buyer's share of the total load. In an uncongested network, this results in a “fair” allocation of costs, i.e., an allocation proportional to the benefits that each party accrues. However, in a congested network, this is no longer the case, as price separation occurs between nodes. In this paper, we therefore propose a cost allocation method based on LMP sensitivity that accounts for the effect of congestion on the distribution of benefits between nodes with different LMPs. Since this sensitivity-based method only takes into account the cost allocation per node, we also propose a means of allocating costs between individual load serving entities (LSEs) at a single node. Due to this refinement, LSEs are rewarded according to their individual contribution to demand response. Finally, we define a fairness index to evaluate the performance of the proposed method as compared to a load-based allocation. We find that when load reductions are small (1%–3% total load), the fairness index of the proposed method is very close to zero, indicating almost identical benefit to cost ratios for all market participants. Although the fairness index increases with increasing load reductions, results show that even for larger load reductions, the fairness index is still lower for the proposed method than for the load-based allocation method.

Load-Following Reserves Procurement Considering Flexible Demand-Side Resources Under High Wind Power Penetration

The variable and uncertain nature of the leading renewable energy resources, such as wind power generation, imposes the development of a sophisticated balance mechanism between supply and demand to maintain the consistency of a power system. In this study, a two stage stochastic programming model is proposed to procure the required load-following reserves from both generation and demand side resources under high wind power penetration. Besides, a novel load model is introduced to procure flexible reserves from industrial clients. Load following reserves from load serving entities (LSE) are also taken into account as well as network constraints, load shedding and wind spillage. The proposed methodology is applied to an illustrative test system, as well as to a 24-node system.

Evaluation of price‐sensitive loads’ impacts on transmission network congestion using an analytical approach

An analytical approach is presented to evaluate the impacts of price-sensitive loads on congestion in transmission network. To achieve this aim, the Lagrange multipliers (LMs) of transmission lines’ power flow constraints are calculated from independent system operator's social welfare maximisation in which demand-side bidding is considered. It is shown that the LM of each congested line can be decomposed into five components. The first part is a constant value for the specified line, while the next two components are associated to generating units and the last two constitutive parts are related to load service entities (LSEs). The proposed decomposition obtains considerable information regarding the impacts of price-sensitive loads on congestion in transmission network. First, the sensitivity of congestion degree of each congested line to the bidding strategies and maximum price-sensitive demand of LSEs are indicated by weighting coefficients of the last two terms in the decomposed LM. Furthermore, the decomposition of LM to the constitutive components reveals the contribution of each generating unit and LSE to the congestion of corresponding line. The simulation results on a test system confirm the efficiency of the proposed approach.

Generation bidding game with potentially false attestation of flexible demand

Abstract With the onset of large numbers of energy-flexible appliances, in particular plug-in electric and hybrid-electric vehicles, a significant portion of electricity demand will be somewhat flexible and accordingly may be responsive to changes in electricity prices. In the future, this increased degree of demand flexibility (and the onset of only short-term predictable intermittent renewable supply) will considerably exceed present level of uncertainty in day-ahead prediction of assumed inelastic demand. For such a responsive demand idealized, we consider a deregulated wholesale day-ahead electricity marketplace wherein bids by generators (or energy traders) are determined through a Nash equilibrium via a common clearing price (i.e., no location marginality). This model assumes the independent system operator (ISO) helps the generators to understand how to change their bids to improve their net revenue based on a model of demand-response. The model of demand-response (equivalently, demand-side bidding day ahead) is based on information from load-serving entities regarding their price-flexible demand. We numerically explore how collusion between generators and loads can manipulate this market. The objective is to learn how to deter such collusion, e.g., how to set penalties for significant differences between stated and actual demand, resulting in higher energy prices that benefit certain generators.

A heuristic ranking approach on capacity benefit margin determination using Pareto-based evolutionary programming technique.

This paper introduces a novel multiobjective approach for capacity benefit margin (CBM) assessment taking into account tie-line reliability of interconnected systems. CBM is the imperative information utilized as a reference by the load-serving entities (LSE) to estimate a certain margin of transfer capability so that a reliable access to generation through interconnected system could be attained. A new Pareto-based evolutionary programming (EP) technique is used to perform a simultaneous determination of CBM for all areas of the interconnected system. The selection of CBM at the Pareto optimal front is proposed to be performed by referring to a heuristic ranking index that takes into account system loss of load expectation (LOLE) in various conditions. Eventually, the power transfer based available transfer capability (ATC) is determined by considering the firm and nonfirm transfers of CBM. A comprehensive set of numerical studies are conducted on the modified IEEE-RTS79 and the performance of the proposed method is numerically investigated in detail. The main advantage of the proposed technique is in terms of flexibility offered to an independent system operator in selecting an appropriate solution of CBM simultaneously for all areas.

Dynamic Incentive Design for Participation in Direct Load Scheduling Programs

Interruptible Load (IL) programs have long been an accepted measure to intelligently and reliably shed demand in case of contingencies in the power grid. However, the emerging market for Electric Vehicles (EV) and the notion of providing non-emergency ancillary services through the demand side have sparked new interest in designing direct load scheduling programs that manage the consumption of appliances on a day-to-day basis. In this paper, we define a mechanism for a Load Serving Entity (LSE) to strategically compensate customers that allow the LSE to directly schedule their consumption, every time they want to use an eligible appliance. We study how the LSE can compute such incentives by forecasting its profits from shifting the load of recruited appliances to hours when electricity is cheap, or by providing ancillary services, such as regulation and load following. To make the problem scalable and tractable we use a novel clustering approach to describe appliance load and laxity. In our model, customers choose to participate in this program strategically, in response to incentives posted by the LSE in publicly available menus. Since 1) appliances have different levels of demand flexibility; and 2) demand flexibility has a time-varying value to the LSE due to changing wholesale prices, we allow the incentives to vary dynamically with time and appliance cluster. We study the economic effects of the implementation of such program on a population of EVs, using real-world data for vehicle arrival and charge patterns.

Optimal Residential Demand Response in Distribution Networks

Demand response (DR) enables customers to adjust their electricity usage to balance supply and demand. Most previous works on DR consider the supply-demand matching in an abstract way without taking into account the underlying power distribution network and the associated power flow and system operational constraints. As a result, the schemes proposed by those works may end up with electricity consumption/shedding decisions that violate those constraints and thus are not feasible. In this paper, we study residential DR with consideration of the power distribution network and the associated constraints. We formulate residential DR as an optimal power flow problem and propose a distributed scheme where the load service entity and the households interactively communicate to compute an optimal demand schedule. To complement our theoretical results, we also simulate an IEEE test distribution system. The simulation results demonstrate two interesting effects of DR. One is the location effect, meaning that the households far away from the feeder tend to reduce more demands in DR. The other is the rebound effect, meaning that DR may create a new peak after the DR event ends if the DR parameters are not chosen carefully. The two effects suggest certain rules we should follow when designing a DR program.

Lower Partial Moment and Information Entropy-Based Dynamic Electricity Purchasing Strategy

In the electricity market environment, load-serving entities (LSEs) will inevitably face risks in purchasing electricity because there are a plethora of uncertainties involved. To maximize profits and minimize risks, LSEs need to develop an optimal strategy to reasonably allocate the purchased electricity amount in different electricity markets such as the spot market, bilateral contract market, and options market. Because risks originate from uncertainties, an approach is presented to address the risk evaluation problem by the combined use of the lower partial moment and information entropy (LPME). The lower partial moment is used to measure the amount and probability of the loss, whereas the information entropy is used to represent the uncertainty of the loss. Electricity purchasing is a repeated procedure; therefore, the model presented represents a dynamic strategy. Under the chance-constrained programming framework, the developed optimization model minimizes the risk of the electricity purchasing portfolio in different markets because the actual profit of the LSE concerned is not less than the specified target under a required confidence level. Then, the particle swarm optimization (PSO) algorithm is employed to solve the optimization model. Finally, a sample example is used to illustrate the basic features of the developed model and method.

Optimal Scheduling of Critical Peak Pricing Considering Wind Commitment

Demand response has been widely implemented as one of the “virtual” control mechanisms to make peak load management more efficient and economic. One of the popular demand response programs is called critical peak pricing (CPP). Relatively simple pricing schemes and convenient implementation within current energy system metering infrastructure make it well accepted by many utilities and load-serving entities (LSEs). In this paper, we investigate the optimal scheduling of CPP events from the perspective of an LSE which has wind energy to sell into the day-ahead market. The goal is to minimize the total operational cost for the whole planning horizon, taking into account the energy purchasing cost, revenue from the CPP, and wind energy sales, as well as imbalance penalties due to wind energy over- and under-commitments. We propose a multi-stage stochastic mixed integer nonlinear programming model. In addition, we perform various analyses of both the special case of a single-stage problem and the general multi-stage problem analytically and experimentally. Our analysis leads to useful operational insights and policy implications on how to manage a renewable-integrated system more efficiently.

Could Low Carbon Capacity Standards be More Cost Effective at Reducing CO2 than Renewable Portfolio Standards?

Abstract We examine the implications of lowering electricity sector CO 2 emissions in PJM through a Low Carbon Capacity Standard (LCCS) instead of a renewables portfolio standard (RPS). An LCCS would create a requirement for load-serving entities to procure new low carbon capacity (GW). The LCCS would provide a greater balance of energy and capacity supply than a renewable portfolio standard, which requires only the supply of energy (and excludes non-renewable low carbon generators). Approximately 25 GW of PJM generation capacity is scheduled to retire by 2019 and the RPSs currently in place in PJM will supply only 5 GW of Equivalent Load Carrying Capability (ELLC). An LCCS, providing the same amount of low carbon energy, would supply 13 to 16 GW of ELCC. We estimate the required reduction of capacity prices required to cover the investment cost premium low carbon capacity would require from consumers. For example, if the energy from an LCCS costs on average $20/MWh more than energy from an RPS, the annual premium would be approximately $2.2 B. In order for consumers to be better off with an LCCS in this example, capacity prices would have to decrease by $40/MW-day. We find that if an LCCS were adopted, coal fired power plants with carbon capture, utilization, and sequestration (CCUS) would likely have the lowest cost per MW of capacity of all low carbon technologies, based on net Cost of New Entry (CONE) estimates.

Hardware Design of Smart Home Energy Management System With Dynamic Price Response

The smart grid initiative and electricity market operation drive the development known as demand-side management or controllable load. Home energy management has received increasing interest due to the significant amount of loads in the residential sector. This paper presents a hardware design of smart home energy management system (SHEMS) with the applications of communication, sensing technology, and machine learning algorithm. With the proposed design, consumers can easily achieve a real-time, price-responsive control strategy for residential home loads such as electrical water heater (EWH), heating, ventilation, and air conditioning (HVAC), electrical vehicle (EV), dishwasher, washing machine, and dryer. Also, consumers may interact with suppliers or load serving entities (LSEs) to facilitate the load management at the supplier side. Further, SHEMS is designed with sensors to detect human activities and then a machine learning algorithm is applied to intelligently help consumers reduce total payment on electricity without or with little consumer involvement. Finally, simulation and experiment results are presented based on an actual SHEMS prototype to verify the hardware system.

Three Simple Steps to Clip the Peak in the Texas (ERCOT) Electricity Market

Electricity resource adequacy is the most urgent and controversial challenge facing the Electric Reliability Council of Texas (ERCOT) market. The policy discussions have neglected some very simple steps that could be taken to promote demand response and reduce peak demand. An allocation of some of the responsibility for non-spinning reserves based on the contribution of a load serving entity (LSE) to peak demand would encourage LSEs to undertake programs to reduce their contribution to the peak. An extension of the four coincident peak (4 CP) pricing used to charge large industrial energy consumers for transmission services to smaller commercial and residential loads would encourage smaller consumers to reduce their peak usage. Narrowing the definition of “peak demand” in the Public Utility Commission of Texas (PUCT) energy efficiency rules would encourage energy efficiency measures better focused on peak demand reduction.