Energy Economics

Abstract

Deregulation in power systems has created new uncertainties and increased the previous ones. The presence of these uncertainties causes the transmission network to remain monopoly and the private investors not being interested in investing in this section. This paper presents a new merchant-based transmission expansion planning (TEP) formulation from the viewpoint of private investors. The information-gap decision theory (IGDT) is used to model the inherent uncertainties associated with the estimated investment cost of candidate lines and the forecasted system load and NSGAII is utilized to solve the multi-objective optimization problem.This algorithm helps private investors to select the best lines for investment in the presence of uncertainties. In order to verify the effectiveness of the proposed method, it has been applied to the IEEE RTS 24-bus system and the simplified Iranian 400-kV transmission system.

Author Keywords

information-gap decision theory; private investors; transmission expansion planning; uncertainties

Abstract

This paper presents an optimal bidding strategy for industrial consumers with cogeneration facilities, power-only and heat-only units to participate in day-ahead electricity market. A information gap decision theory (IGDT) technique is implemented for determining the optimal bidding strategies considering market price uncertainty. IGDT evaluates the robustness/opportunity of optimal bidding strategy under market price uncertainty considering the consumer choice of taking risk-averse or risk-taking decisions. It is confirmed that the risk-averse or risk-taking decisions might affect the expected profit and bidding curve of the consumers. Moreover, demand response (DR) program has been implemented in order to serve the power and heat demands of the consumer with minimum cost. In the proposed DR program, the total power and heat demand of consumer will be supplied without any curtailed load. The responsive load can vary in different time intervals. In addition, it is assumed that the consumer will pay (receive) for increased (reduced) consumption, which is proportional to the day-ahead market price. In this paper, the heatpower dual dependency characteristic in different types of combined heat and power (CHP) units is taken into account and all technical constraints of generation units are satisfied. In addition, a heat buffer tank with the ability of heat storage is incorporated in the proposed framework. The verification of the proposed method is demonstrated using the simulation of a case study.

Keywords

Combined heat and power (CHP) system, Demand response programs, Feasible operation region of CHP units, CHP optimal bidding strategy, Information gap decision theory (IGDT).

Abstract

Demand response and real-time pricing of electricity are key factors in a smart grid as they can increase economic efficiency and technical performances of power grids. This paper focuses on incorporating price-responsive customers in day-ahead scheduling of smart distribution networks under a dynamic pricing environment. A novel method is proposed and formulated as a tractable mixed integer linear programming optimization problem whose objective is to find hourly sale prices offered to customers, transactions (purchase/sale) with the wholesale market, commitment of distribution generation units, dispatch of battery energy storage systems and planning of interruptible loads in a way that the profit of the distribution network operator is maximized while customers’ benefit is guaranteed. To hedge distribution network operator against financial risk arising from uncertainty of wholesale market prices, a risk management model based on a bi-level information-gap decision theory is proposed. The proposed bi-level problem is solved by recasting it into its equivalent single-level robust optimization problem using Karush-Kuhn-Tucker optimality conditions. Performance of the proposed model is verified by applying it to a modified version of the IEEE 33-bus distribution test network. Numerical results demonstrate the effectiveness and efficiency of the proposed method.

Keywords

Demand response; Real-time pricing; Information-gap decision theory; Smart distribution network; Risk management; Uncertainty.

Highlights

• Proposing a model for incorporating price-responsive customers in day-ahead scheduling of smart distribution networks; this model provides a win–win situation.
• Introducing a risk management model based on a bi-level information-gap decision theory and recasting it into its equivalent single-level robust optimization problem using Karush-Kuhn-Tucker optimality conditions.
• Utilizing mixed-integer linear programing formulation that is efficiently solved by commercial optimization software.

Abstract

In the context of restructured electricity market and smart grid, uncertainties including renewable generation, load demand, and electricity price would significantly affect the technical and financial aspects of smart distribution networks. This paper presents a risk-averse decision making tool to help distribution network operator (DNO) in short-term operational activities. The objective is to optimize hourly sale prices offered to the customers, transactions (purchase/sale) with the wholesale market, commitment of distributed generation, dispatch of energy storage systems, and planning of interruptible loads in a way that a target profit for the risk-averse DNO is guaranteed. A bi-level information gap decision theory (IGDT) inspired problem is developed to hedge the DNO against risk imposed by the information gap between the forecasted and actual uncertain variables. The bi-level problem is recast into its equivalent single level problem driven by Karush-Kuhn-Tucker optimality conditions. Since the uncertain variables compete with each other to maximize their enveloped-bounds, the augmented ε-constraint method is used to solve the proposed IGDT-inspired multi-objective optimization problem. A Monte Carlo simulation based after-the-fact analysis is conducted to verify the robust performance of the operational decisions. The effectiveness and efficiency of the proposed model are evaluated on the 33-bus and the 118-bus modified test networks.

Keywords

Augment ε-constraint method; Information gap decision theory; Short-term operation; Smart distribution network; Risk-averse; Uncertainty modelling

Highlights

• A risk-averse decision making tool is proposed to help DNOs in short-term activities.
• Wind generation, load demand, and electricity price are considered uncertain.
• IGDT-based and augment ε-constraint methods are presented to manage uncertainties.
• Demand response programs are incorporated into the proposed model.
• The proposed model is a mixed-integer linear programming optimization problem.

Abstract

This paper presents a dynamic transmission expansion planning framework with considering load uncertainty based on Information-Gap Decision Theory. Dynamic transmission planning process is carried out to obtain the minimum total social cost over the planning horizon. Robustness of the decisions against under-estimated load predictions is modeled using a robustness function. Furthermore, an opportunistic model is proposed for risk-seeker decision making. The proposed IGDT-based dynamic network expansion planning is formulated as a stochastic mixed integer non-linear problem and is solved using an improved standard branch and bound technique. The performance of the proposed scheme is verified over two test cases including the 24-bus IEEE RTS system and Iran national 400-kV transmission network.

Keywords

Information-Gap Decision Theory, Dynamic transmission expansion planning, Load uncertainty, Robustness model, Opportunistic model, Stochastic optimization

Abstract

This paper presents a risk-constrained programming approach to solve a retailer’s medium-term planning problem. A retailer tries to maximize its profit via determining the optimal price offered to the customers as well as optimal strategy of participating in futures and pool markets. The uncertainty of pool prices is modeled by an envelope-bound information-gap model. Another source of uncertainty in this problem is the clients’ demand, which is considered via a scenario generation method. The proposed method is formulated as a bi-level stochastic programming problem based on the information-gap decision theory. The Karush–Kuhn–Tucker optimality conditions are used to convert the bi-level problem into a single-level robust optimization problem. The performance of the proposed method is demonstrated using a case study of the New England market, and results are discussed.

Keywords

retailer’s planning; information-gap decision theory; price–quota curve; forward contracting; price uncertainty.

Abstract

In this paper, a robust bi-level decision-making framework is presented for distributed generation (DG) owning retailers to supply the electricity to price-sensitive customers. Uncertainties about client demand and wholesale prices are the main difficulties faced by the electricity retailer. Clients can adjust their consumption according to the retailer’s selling price. A higher selling price increases retailers’ profit but decreases client consumption. Hence, the retailer faces a tradeoff between the price and sales. In the proposed model, the optimal selling price and the retailer’s energy-supply strategy are modeled in the lower sub-problem. According to the proposed selling price, the optimal energy consumption of price-sensitive clients is determined in the upper sub-problem. To evaluate the financial risk arising from uncertain prices, the Information Gap Decision Theory (IGDT) approach is addressed in the lower subproblem. Additionally, the risk-based optimization problem is formulated for risk-averse and risk-taker retailers via the robustness and opportunity functions, respectively. The robustness of the optimal solution against price variations is evaluated such that the associated profit will be more than the electricity retailer’s acceptable threshold. The efficiency and performance of the decision-making framework are analyzed via a case study, and the numerical results are discussed.

Keywords

Distributed generation, Elasticity, Electricity retailer, Information gap decision theory, Strategic risk management, Optimization

Abstract

In a competitive market where all producers must participate in the market, WPPs (wind power producers) face two sources of uncertainty: (i) future market prices, and (ii) their production capability in coming hours. In this paper a risk-constrained optimal self-scheduling method for a WPP considering the uncertainty associated with market prices and wind generation is proposed. IGDT (Information Gap Decision Theory) is used to address theses uncertainties in WPP’s self-scheduling. The proposed IGDT-based model is a bilevel programming approach, which is transformed to an equivalent single level bilinear programming model that can be solved using available solvers. Numerical simulations and discussions are provided.

Keywords

IGDT (Information-Gap Decision Theory), Electricity markets, Self-scheduling, Uncertainty, Wind producer

Abstract

In restructured electricity market, the consumers have various procurement strategies to supply their electricity demand from alternative resources. An approach to cost reduction and risk management for a large consumer is participation in demand response programs (DRP). Due to uncertain nature of pool prices and price fluctuations in pool market, uncertainty modeling is inevitable. For evaluation of different procurement strategies of large consumer, a technique based on information gap decision theory (IGDT) is proposed. This paper develops an energy acquisition model for large consumer with multiple procurement options including distributed generation (DG), bilateral contracts and pool market purchase with participating in DRP. This paper is focused to study the effect of DRP on the procurement strategy problem. Also, the time-of-use (TOU) rates of demand response programs have been modeled and consequently its influence on load profile and procurement strategy has been discussed. The proposed method does not minimize the procurement cost, but allows deriving robust decision with respect to price volatility. The robustness of procurement strategies for high procurement costs is optimized and related procurement strategy is proposed. The proposed method either deals with optimizing the opportunities to take advantage from low procurement costs or low pool prices. Finally, a case study is studied to show the advantages of proposed method.

Keywords

Large consumer; Electricity procurement strategy; Information gap decision theory (IGDT); Demand response programs (DRP)

Highlights

• Two procurement objective functions for a large consumer considering DRP are proposed.
• The new load modeling and formulation for DRP has been proposed.
• The procurement strategies of large consumer, a technique based on IGDT is proposed.
• The proposed method does not minimize the procurement cost but allows deriving robust decision with respect to price volatility.
• The proposed method either deals with optimizing the opportunities to take advantage from low pool prices.

Abstract

This paper considers a price-taker generation station producer that participates in a day-ahead market. The producer behaves as a price-taker participant in the day-ahead electricity market. In electricity market, the price-taker producer could develop bidding strategies to maximize own profits. While making optimal bidding strategy, the market price uncertainty needs to be considered as they have direct impact on the expected profit and bidding curves. In this paper, a hybrid approach based on information gap decision theory (IGDT) and modified particle swarm optimization (MPSO) is used to develop the optimal bidding strategy. Information gap decision theory is used to model the optimal bidding strategy problem. It assesses the robustness/opportunity of optimal bidding strategy in the face of the market price uncertainty while price-taker producer considers whether a decision risk-averse or risk-taking. The optimization problems to delivering IGDT approach are solved using MPSO. It is shown that risk-averse or risk-taking decisions might affect the expected profit and bidding curve to day-ahead electricity market. The IGDT–MPSO method is illustrated through a case study and compared to IGDT-MINLP method.

Keywords

Optimal bidding strategy, Information gap decision theory (IGDT), Modified particle swarm optimization (MPSO), Market price uncertainty

Abstract

In this paper, a new transmission expansion planning (TEP) model considering wind farms (WFs) optimal integration to power systems is proposed based on the information-gap decision theory (IGDT). The uncertainties of WFs output power and forecasted demand are considered in the problem, and IGDT is used to control the investment risk as well as to reduce the effects of these uncertainties on the investors’ strategies. The TEP model is formulated for the risk-averse and risk-seeker investors through the robustness and opportunity models, respectively. Moreover, this TEP model allows WF lines and network lines to be added at multiple time points during a multi-stage time horizon. A genetic algorithm approach is employed to solve the bi-level IGDT-based optimization problem. Finally, this IGDT-based model is applied to the simplified Iranian 400-kV system, and the results are discussed.

Keywords

Information-gap decision theory (IGDT); multi-stage approach; optimal wind farm integration; transmission expansion planning; uncertainty

Abstract

An independent generation company (GenCo) secures its future trading position by managing its portfolio among multiple trading options. Future returns of these trading options are not known during decision making and are traditionally estimated using probabilistic or fuzzy methods. Quantifying such uncertainty of market returns by conventional methods does not reflect the information gap existing between estimated and actual market returns. Based on quantification of this information gap, the paper proposes GenCo’s portfolio optimization using a non-probabilistic Information Gap Decision Theory (IGDT). This framework comprehensively models GenCo’s behavior in deciding its trading strategy. Considering GenCo’s risk-averse behavior, the framework provides decisions that are robust towards losses, while considering its risk-seeking behavior the framework offers opportunity to capture windfall gains. The proposed approach has been validated through practical case study of PJM market.

Abstract

Fossil fuel gencos are subject to influence of multiple uncertain but interactive energy and emission markets. It procures production resources from fuel and emission market and sells its generation through multiple contracts in electricity market. With increasing volatility and unpredictability in energy markets, a genco needs to make prudent decision to manage its trading in all involved markets, to guarantee minimum profit. Considering the existing market uncertainties and associated information gap, this paper proposes a robust decision making approach for gencos trading portfolio selection in all three involved markets, based on Information Gap Decision Theory (IGDT). Results from a realistic case study provides a range of decisions for a risk averse genco, appropriate to its nature, and based on the trade-off existing between robustness and targeted profit.

Abstract

In midterm planning, the objective of an electricity retailer is to manage a portfolio of different contracts and to determine the selling price offered to its clients. This paper provides a novel technique based on Information Gap Decision Theory (IGDT) to assess different strategies for a retailer under unstructured pool price uncertainty. This method can be used as a tool for assessing the risk levels, considering whether a retailer is risk-taking or risk-averse regarding its midterm strategies. Supply sources include forward contracts, a limited self-generating facility, and the pool. It is shown that in robust strategy, procurement from sources with uncertain prices decreases. Also, the selling price offered to the consumers rises, decreasing the actual demand of the retailer, and consequently the expected profit is decreased. A case study is used to illustrate the proposed technique.

Keywords

Retailer; Risk; Information Gap Decision Theory; Energy procurement; Selling price

Highlights

• A midterm decision model is introduced for an electricity retailer using IGDT.
• This tool is used to assess the risk levels under unstructured pool price uncertainty.
• The proposed method provides a robust procurement/selling price strategy.
• The method optimizes the robustness function to gain minimum acceptable profit.
• The proposed method optimizes the opportunities to take advantage of high profits.

Abstract

This paper provides a technique to derive the bidding strategy in the day-ahead market for a large consumer that procures its electricity demand in both day-ahead market and a subsequent adjustment market. It is considered that hourly market prices are normally distributed and this correlation is modeled by variance-covariance matrix. The uncertainty of procurement cost is modeled using concepts derived from information gap decision theory which allows deriving robust bidding strategies with respect to price volatility. First Order Reliability Method is applied to construct the robust bidding curve. The proposed technique is illustrated through a realistic case study.

Keywords

Bidding strategy; First order reliability method; Information gap decision theory; Large consumer

Abstract

This paper provides a technique to derive the bidding strategy in the day-ahead market of a large consumer that procures its electricity demand in both the day-ahead market and a subsequent adjustment market. Price uncertainty is modeled using concepts derived from information gap decision theory, which allows deriving robust decisions with respect to price volatility. Risk aversion is built implicitly within the proposed model. Correlations among prices in the day-ahead and the adjustment markets are properly modeled. The proposed technique is illustrated through a realistic case study.

Abstract

A new risk-constrained bidding curve construction method is presented in this paper. A Day-ahead energy market has been chosen for competition of GenCos and the Information Gap Decision Theory (IGDT) is used for modelling the Day-ahead market price uncertainty and its corresponding risk. The bilateral contracts of the GenCo are also considered in the proposed framework. A Bi-level optimization problem is incorporated in the proposed method to guarantee a pre-specified level of revenue. The proposed IGDT based method constructs the non-decreasing bidding curve while dispatching units based on the uncertain forecasted prices of the next-day market. The verification of the proposed method is demonstrated by simulation of a GenCo with 5 thermal units in various day-ahead markets. © 2013 IEEE.

Keywords

Bi-level optimization; Bilateral contracts; Construction method; Day ahead market; Day-ahead energy markets; Information gap; Strategic bidding; Thermal units; Commerce; Electrical engineering; Uncertainty analysis; Risk assessment.

Abstract

The present study presents a new risk-constrained bidding strategy formulation of large electric utilities in, presence of demand response programs. The considered electric utility consists of generation facilities, along with a retailer part, which is responsible for supplying associated demands. The total profit of utility comes from participating in day-ahead energy markets and selling energy to corresponding consumers via retailer part. Different uncertainties, such as market price, affect the profit of the utility. Therefore, here, attempts are made to make use of Information Gap Decision Theory (IGDT) to obtain a robust scheduling method against the unfavorable deviations of the market prices. Implementing demand response programs sounds attractive for the consumers through providing some incentives in one hand, and it improves the risk hedging capability of the utility on the other hand. The proposed method is applied to a test system and effect of demand response programs is investigated on the total profit of the utility.

Keywords

Bidding strategy; Risk management; Information Gap Decision Theory (IGDT); Demand response; Electric utility.

Highlights

• We investigate the effect of demand response on GenCo’s bidding strategy.
• A proposed IGDT method is presented for risk management.
• The bidding formulation of a company consisted of generation facility along with a retailer part is presented.
• A linear programming format of IGDT method is presented.

Abstract

This paper presents the application of information gap decision theory (IGDT) to help the distribution network operators (DNOs) in choosing the supplying resources for meeting the demand of their customers. The three main energy resources are pool market, distributed generations (DGs), and the bilateral contracts. In deregulated environment, the DNO is faced with many uncertainties associated to the mentioned resources which may not have enough information about their nature and behaviors. In such cases, the classical methods like probabilistic methods or fuzzy methods are not applicable for uncertainty modeling because they need some information about the uncertainty behaviors like probability distribution function (PDF) or their membership functions. In this paper, a decision making framework is proposed based on IGDT model to solve this problem. The uncertain parameters considered here, are as follows: price of electricity in pool market and demand of each bus. The robust strategy of DNO is determined to hedge him against the risk of increasing the total cost beyond what it is willing to pay. The effectiveness of the proposed tool is assessed and demonstrated by applying it on a large distribution network.

Keywords

Bilateral contracts; distributed generation; information gap decision theory; risk; uncertainty

Abstract

This paper considers a price-taker generation company to participate in day-ahead electricity energy market. While making optimal bidding strategy for producer, factors such as the characteristics of generator and the market price uncertainty need to be considered because of having direct impact on the expected profit and bidding curve. The market price considered an uncertain variable and it is assumed that the generation company forecasted the market prices. In this study, the uncertainty model of market price is considered based on the concept of weighted average squared error using a variance–covariance matrix. Information gap decision theory is used to develop the bidding strategy of a generation company. It assesses the robustness/opportunity of optimal bidding strategy in the face of the market price uncertainty while producer considers whether a decision risk-averse or risk-taking. It is shown that risk-averse or risk-taking decisions might affect the expected profit and bidding curve to day-ahead electricity market. A case study is used to illustrate the proposed approach.

Keywords

Optimal bidding strategy, Information gap decision theory, Risk management

Highlights

• A new model for optimal bidding strategy of generation companies is proposed based on IGDT.
• This methodology is not based on profit maximization; however, it results in a risk-averse and risk-taking issue.
• The generating company can maximize the robustness of its bidding strategy against low market prices.
• Also it can be gained from the opportunity of increasing the market prices.

Abstract

This paper considers a profit-maximizing thermal unit producer that participates in a day-ahead market. The producer behaves as a price-taker in the day-ahead electricity market. This paper provides the information gap decision theory for determining the optimal bidding strategies for the day-ahead market. While making bidding strategy, factors such as characteristics of the generator and market price uncertainty need to be considered as they have direct impact on the expected profit and bidding curve. In this paper, a method of building an optimal bidding strategy is presented under market price uncertainty using information gap decision theory (IGDT). Information gap decision theory is a non-probabilistic decision theory that seeks to optimize robustness to failure – or opportunity to windfall – under severe uncertainty. It is shown that risk-aversion and risk-taker may influence the expected profit and bidding curve of a producer. The proposed method is illustrated through a realistic case study.

Keywords

Optimal bidding strategy; Information gap decision theory (IGDT); Uncertainty

Highlights

• A new model for optimal bidding strategy of generation companies is proposed based on IGDT.
• This methodology is not based on profit maximization; however, it results in a risk-averse and risk-taking issue.
• The generating company can maximize the robustness of its bidding strategy against low market prices.
• Also it can be gained from the opportunity of increasing the market prices.