Deregulated Power Market Research Articles

Multi-objective congestion management incorporating voltage and transient stabilities

Congestion in a power system is turned up due to operating limits. To relieve congestion in a deregulated power market, the system operator pays to market participants considering their bids to alter their active powers. After relieving congestion, the network may be operated with a reduced voltage or transient stability margin because of hitting security limits or increasing the contribution of risky participants. The proposed multi-objective framework for congestion management in this paper simultaneously optimizes competing objective functions of congestion management cost, voltage security, and dynamic security. The voltage stability margin and corrected transient energy margin are employed as indices to be incorporated into the multi-objective congestion management. A fuzzy decision maker is proposed to derive the most efficient solution among Pareto-optimal solutions of multi-objective mathematical programming problem. Results of testing the proposed method on the New-England test system elaborate the efficiency of the proposed method.

Congestion management considering voltage security of power systems

Congestion in a power network is turned up due to system operating limits. To relieve congestion in a deregulated power market, the system operator pays to market participants, GENCOs and DISCOs, to alter their active powers considering their bids. After performing congestion management, the network may be operated with a low security level because of hitting some flows their upper limit and some voltages their lower limit. In this paper, a novel congestion management method based on the voltage stability margin sensitivities is introduced. Using the proposed method, the system operator so alleviates the congestion that the network can more retain its security. The proposed method not only makes the system more secure after congestion management than other methods already presented for this purpose but also its cost of providing security is lower than the earlier methods. Test results of the proposed method along with the earlier ones on the New-England test system elaborate the efficiency of the proposed method from the viewpoint of providing a better voltage stability margin and voltage profile as well as a lower security cost.

Distribution Feeder Scheduling Considering Variable Load Profile and Outage Costs

In a deregulated power market, customers would have more choices for their power service and the improvement of service quality has become a challenge to power transmission and distribution companies. Distribution system reliability that was traditionally considered within the planning activities is now incorporated in the operational environment. This paper presents study results of a multiobjective feeder operation optimization problem that considers how to balance network efficiency, switching and reliability costs in a distribution network. The proposed method divides annual feeder load curve into multiperiods of load levels and optimizes the feeder configurations for different load levels in annual operation planning. Customer load profiles and seasonal varying data of feeder section failure rates and customer interruption costs are considered. Numerical simulations demonstrate the time-varying effects on the optimal distribution feeder configuration and operation costs. A binary particle swarm optimization (BPSO) search is adopted to determine the feeder switching schedule. Test results indicate that not considering time-varying effects and using only simplified fixed load and reliability parameters could underestimate the total loss to the utility and its customers.

A dynamic regrouping based sequential dynamic programming algorithm for unit commitment of combined heat and power systems

This paper addresses the unit commitment (UC) in multi-period combined heat and power (CHP) production planning under the deregulated power market. In CHP plants (units), generation of heat and power follows joint characteristics, which implies that it is difficult to determine the relative cost efficiency of the plants. We introduce in this paper the DRDP-RSC algorithm, which is a dynamic regrouping based dynamic programming (DP) algorithm based on linear relaxation of the ON/OFF states of the units, sequential commitment of units in small groups. Relaxed states of the plants are used to reduce the dimension of the UC problem and dynamic regrouping is used to improve the solution quality. Numerical results based on real-life data sets show that this algorithm is efficient and optimal or near-optimal solutions with very small optimality gap are obtained.

Assessment of dynamic available transfer capability for deregulated power industry

This paper describes the assessment of dynamic Available Transfer Capability (ATC) by considering transient rotor angle stability. Since the loads and wheeling transactions vary dynamically in the deregulated power market, the power producers must respond quickly to those changes without affecting the system security and stability. This paper presents an application of the Evolutionary Programming (EP) algorithm to determine the optimal generation dispatch of power producers with dynamic security constraints. The proposed algorithm is demonstrated to determine dynamic ATC for practical IEEE-30 and Indian utility-62 bus systems with changing load conditions.

A variant of the dynamic programming algorithm for unit commitment of combined heat and power systems

Abstract The paper addresses the unit commitment in multi-period combined heat and power (CHP) production planning under the deregulated power market. In CHP plants (units), generation of heat and power follows joint characteristics, which means that production planning must be done in coordination. We introduce in this paper the DP-RSC1 algorithm, which is a variant of the dynamic programming (DP) algorithm based on linear relaxation of the ON/OFF states of the units and sequential commitment of units one by one . The time complexity of DP-RSC1 is proportional to the number of generating units in the system, the number of periods over the planning horizon and the time for solving a single-period economic dispatch problem. We have compared the DP-RSC1 algorithm with realistic power plants against the unit decommitment algorithm and the traditional priority listing method. The results show that the DP-RSC1 algorithm gives somewhat more accurate results (0.08–0.5% on average, maximum 10% for the individual sub-case) and executes 3–5 times faster on average than the unit decommitment algorithm. It is not surprising that the solution quality of the DP-RSC1 algorithm is much better than that of the priority listing method.

Short‐term load forecasting using informative vector machine

AbstractIn this paper, a novel method is proposed for short‐term load forecasting, which is one of the important tasks in power system operation and planning. The load behavior is so complicated that it is hard to predict the load. The deregulated power market is faced with the new problem of an increase in the degree of uncertainty. Thus, power system operators are concerned with the significant level of load forecasting. Namely, probabilistic load forecasting is required to smooth power system operation and planning. In this paper, an IVM (Informative Vector Machine) based method is proposed for short‐term load forecasting. IVM is one of the kernel machine techniques that are derived from an SVM (Support Vector Machine). The Gaussian process (GP) satisfies the requirements that the prediction results are expressed as a distribution rather than as points. However, it is inclined to be overtrained for noise due to the basis function with N2 elements for N data. To overcome this problem, this paper makes use of IVM that selects necessary data for the model approximation with a posteriori distribution of entropy. That has a useful function to suppress the excess training. The proposed method is tested using real data for short‐term load forecasting. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 166(2): 23– 31, 2009; Published online in Wiley InterScience (www. interscience.wiley.com). DOI 10.1002/eej.20693

Transmission network expansion planning with security constraints based on bi‐level linear programming

AbstractIn deregulated power market, multiple conflicting objectives with many constraints should be balanced in transmission planning. The primary objective is to ensure the reliable supply to the demand as economically as possible. In this paper, a new bi‐level linear programming model for transmission network expansion planning (TNEP) with security constraints has been proposed. The modeling improves traditional building style by adding reliability planning into economy planning as constraints, letting optimal planning strategy be more economic and highly reliable. A hybrid algorithm which integrates improved niching genetic algorithm and prime‐dual interior point method is newly proposed to solve the TNEP based on bi‐level programming. The advantages of the new methodology include (1) the highest reliability planning scheme can be acquired as economically as possible; (2) new model avoids the contradictions of conflicting objectives in TNEP, and explores new ideas for TNEP modeling; (3) the proposed hybrid algorithm is able to solve bi‐level programming and fully manifests the merits of two algorithms as well. Simulation results obtained from two well‐known systems and comparison analysis reveal that the proposed methodology is valid. Copyright © 2008 John Wiley & Sons, Ltd.

Cost Benefit Analysis of the Power Storage System Considering Outage Cost in the Deregulated Power Market

In this paper, the authors propose the mathematical model which derives the optimal operation strategies of an on-site power storage system through the use of stochastic dynamic programming technique. The model takes account of the variations and uncertainties of electricity market prices as well as the outage costs of power grid failures. The market price fluctuation is modeled with stochastic differential equation. The stochastic state transitions between normal and failed systems are modeled with exponential density functions. The derived optimal operation indicates that the economic value of the storage system may be increased substantially, if the avoided outage costs are explicitly taken into account. The results of the sensitivity analysis indicate that the most influential parameters are the magnitude of outage cost and the mean time to failure of power grid.

An efficient envelope-based Branch and Bound algorithm for non-convex combined heat and power production planning

Combined heat and power (CHP) production is universally accepted as one of the most energy-efficient technologies to produce energy with lower fuel consumption and fewer emissions. In CHP technology, heat and power generation follow a joint characteristic. Traditional CHP production is usually applied in backpressure plants, where the joint characteristic can often be represented by a convex model. Advanced CHP production technologies such as backpressure plants with condensing and auxiliary cooling options, gas turbines, and combined gas and steam cycles may require non-convex models. Cost-efficient operation of a CHP system can be planned using an optimization model based on forecasts for heat load and power price. A long-term planning model decomposes into thousands of single-period models, which can be formulated in the convex case as linear programming (LP) problems, and in the non-convex case as mixed integer programming (MIP) problems. In this paper, we introduce EBB algorithm, for solving the non-convex single-period CHP models of a long-term planning problem under the deregulated power market. EBB is based on the Branch and Bound (B&B) algorithm where tight lower bounds are computed analytically for pruning the search tree and the LP sub-problems are solved through an efficient envelope-based dual algorithm. We compare the performance of EBB with realistic models against the ILOG CPLEX 9.0 MIP solver and the Power Simplex (PS)-based B&B algorithm (PBB). PS is an efficient specialized primal-based Simplex algorithm developed for convex CHP planning problems. EBB is from 661 to 955 times (with average 785) faster than CPLEX and from 11 to 31 times (with average 24) faster than PBB.

Integrating interruptible IPP contracts in the probabilistic production simulation of power market systems

Assuring appropriate levels of reliability at the minimum possible cost using market-based approaches is a contemporary issue that is attracting much attention in deregulated power market systems. Introducing interruptible electricity contracts could provide a possible solution to such a problem. However, theoretical frameworks for examining the impact of these contractual arrangements on system operations have not been maturely developed. This paper presents a framework for modeling and evaluating an interruptible power supply contract for flexible independent power producers based on probabilistic production simulation. The interdependence between the pricing model of the interruptible IPP contract and the operations of the whole system is addressed and solved by an iterative coordinating algorithm. The probabilistic production simulation framework allows the uncertainties inherent in both the forced outages of generators and system load demand at the contract delivery time to be taken into account. The proposed approach is capable of achieving comprehensive evaluations and systematic pricing for the interruptible IPP contract. A numerical case study is presented to illustrate the validity of the methodology.

UPFC sizing by means of an economically correct secure economic dispatch in a deregulated power market scenario

AbstractIn a deregulated power market scenario, the Independent System Operator (ISO) main problem is how to minimize the power system insecurity risk. To face this problem, in previous papers, assuming the insecurity risk as a part of the global cost, a model named Economically correct Secure Economic Dispatch (EcSED) has been proposed by the authors. Further, the EcSED has been improved considering the presence of an Unified Power Flow Controller (UPFC) device in the transmission network. In this paper, by means of an enhanced formulation of the improved EcSED, the size of an UPFC is determined, by including the UPFC investment cost within the objective function. Indeed, an UPFC installed in the transmission network may minimize the insecurity risk, hence the global cost, although its usage implies an expense, function of the size of the device itself. Some numerical experiments on a 5‐bus test system are also reported, to prove the effectiveness of the enhanced EcSED model formulation. Copyright © 2007 John Wiley & Sons, Ltd.

Informative Vector Machineを用いた短期電力負荷予測

In this paper, a novel method is proposed for short-term load forecasting. It is one of important tasks in power system operation and planning. The load behavior is so complicated that it is hard to predict the load. The deregulated power market is faced with a new aspect that the degree of uncertainty increases. Thus, power system operators are concerned with the significant level of load forecasting. Namely, probabilistic load forecasting is required to smooth power system operation and planning. In this paper, an IVM (Informative Vector Machine) based method is proposed for short-term load forecasting. IVM is a one of kernel machine techniques that are derived from SVM (Support Vector Machine). The Gaussian process (GP) satisfies the requirements that the prediction results are expressed in distribution rather than point. However, it is inclined to be overfitting for noise due to the basis function with N2 elements for N data. To overcome the problem, this paper makes use of IVM that selects necessary data for the model approximation with posteriori distribution of entropy. That has a useful function to suppress the overfitting. The proposed method is tested for real data of short-term load forecasting.

A Risk Management Method for the Operation of a Supply-Chain without Storage:-Supply Policy Optimization under Demand Risk-

A business risk management method has been developed for a supply-chain without a storage function under demand uncertainty. Power supply players in the deregulated power market face the need to develop the best policies for power supply from self-production and reserved purchases to balance demand, which is predictable with error. The proposed method maximizes profit from the operation of the supply-chain under probabilistic demand uncertainty on the basis of a probabilistic programming approach. Piece-wise linear functions are employed to formulate the impact of under-booked or over-booked purchases on the supply cost, and constraints on over-demand probability are introduced to limit over-demand frequency on the basis of the demand probability distribution.The developed method has been experimentally applied to the supply policy of a power-supply-chain, the operation of which is based on a 3-stage pricing purchase contract and on 28 time zones. The characteristics of the obtained optimal supply policy are successfully captured in the numerical results, which suggest the applicability of the proposed method.

The Economics of Energy Storage in 14 Deregulated Power Markets

In a regulated market large scale storage of electrical energy, for example by pumped storage, time shifts the generation of power and has been used to defer generation investment. In a deregulated market power storage, when used for energy rather than as a source of spinning or standby reserve or frequency control, is a simple economic proposition: power is purchased during periods of low price and regenerated and resold during period s of high price . In this study historical diurnal price pattern s in 14 deregulated markets are analyzed to give an initial prediction of the economic incentive for energy storage. We rank the 14 markets based on available revenue and potential return on investment: the incentive to store energy varies significantly between markets. The differences between markets arise because of different diurnal pattern s of power price. Diurnal price patterns in turn reflect a comp lex set of factors in a market , including generation mix , market design and participant behaviours.

Using Diurnal Power Price to Configure Pumped Storage

In deregulated power markets, generated power can be time-shifted from a period of low price to that of high price by energy storage with an efficiency penalty. Pumped storage is a common form of energy storage. The total amount of power stored (megawatt-hour) is set by the reservoir capacity. The rate of recovery of stored energy (megawatt) is set by the capacity of the pump/generator and related equipment. A model is developed to maximize the return on investment in pumped storage by varying the generation capacity for a given reservoir to optimize the daily operating hours. Two examples from Alberta, Canada, confirm that the size of pump/generator relative to the reservoir capacity that optimizes daily operating time and return on investment depends on each of the deregulated power market (diurnal price pattern), the site within the power market (site-specific interconnection charges), and the storage technology (energy efficiency)

Valuation of reactive power support services based on sensitivity and risk analysis

In deregulated power market environment system operators need to procure adequate reactive power support services to facilitate real power transactions and improve system security. However, reactive power produced by suppliers at different locations may have different effects on the economics and operating security of the system. Thus, the assessment of the value of these reactive power support services is an important issue to be dealt with. With this objective an evaluation methodology based on voltage sensitivity and risk analysis is proposed from the perspective of voltage regulation. The IEEE 14-node test system is used to illustrate and examine the features of the proposed methodology. Some interesting results are observed and discussed.

An Application of Interval Analysis and Optimization to Electric Energy Markets

In this paper, we address the issue of quantifying model uncertainties in a deregulated power market. We are interested in a worst-case analysis of effects of network parameter uncertainties on optimization problems that are solved on the level of an independent system operator (ISO). The focus is on linear programming (LP) formulations because of their practical relevance. After briefly reviewing applications of different interval analysis tools (interval matrix inversion and various contractors and solvers for systems of interval equations), we propose two tailored algorithms. In the first (basic LP approach), we propose another solver for a system of linear interval equations, while in the second (power system-customized LP approach), we use features of the power system model to reduce the width of the interval solution. The interval-based results are compared with a deterministic robust noninterval matrix inversion-based solution of a model of the benchmark 68-node New England/New York interconnected power system. Presented analyzes demonstrate that even small parameter variations can have significant impact on optimization.

05/02114 An efficient stochastic self-scheduling technique for power producers in the deregulated power market

05/02114 An efficient stochastic self-scheduling technique for power producers in the deregulated power market

Pricing for load and wheeling charge considering transmission paths in deregulated power markets

This paper proposes a practical method to derive reasonable load prices and wheeling charges in a deregulated power market. The proposed pricing model not only ensures the recovery of expense for utilities, but also provides reasonable electricity prices for consumers. A competitive market environment is defined at the beginning of this paper to include independent power producers (IPPs), utilities and customers. The pricing model is formulated mathematically as a nonlinear optimization problem. Based on the optimal solution, the transmission path can be identified by investigating power flow with respect to generator outputs and load demands through sensitivity analysis. The proposed approach is applied to an IEEE-30 bus system, where the simulation results demonstrated that the proposed pricing strategy can also be used for the transmission congestion and loop flow problems.