Probabilistic Production Cost Research Articles

Energy Storage Sizing and Probabilistic Reliability Assessment for Power Systems Based on Composite Demand

This paper presents an energy storage system (ESS) sizing model and reliability assessment framework to quantify reliability improvements due to ESS of electric energy systems with high penetration of renewables. The model formulation takes into account: (a) variable generation (VG) forced outage rates (FORs), (b) reserve and demand requirements, and (c) conventional generation (CG) operational constraints and practices. Correlation coefficients among wind speed, solar radiation, and demand are computed using the least square estimation method (LSE). Depending on the correlation, probabilistic models for the composite demand are computed and integrated into the ESS sizing model. Subsequently, the ESS optimal sizes are computed along with the ESS optimal charging and discharging schedules. Then, the reliability is quantified using the probabilistic production costing (PPC) method. A case study is presented in which one VG was approximated as independent variable because of its low correlation with demand and other VG. The case study compares the error when treating the VG as independent.

탐라해상풍력발전단지의 확률적 시뮬레이션 방법을 이용한 Capacity Credit의 평가

This study is described an algorithm for probabilistic production cost credit evaluation of wind turbine generators (WTG) with multi-state and aims to analysis the reliability of wind power generator in Jeju Island power system in order to maintain the operational state of power system at the reliability level. An analysis on generated capacity of wind power generator with Tamla offshore wind farm, which owned by KOEN(Korea South-East Power Co.Ltd.) and without Tamla offshore wind farm has been done in terms of Effective Load Carrying Capability(ELCC) and Capacity Credit(C.C.) in power system from the perspective of Loss of Load Expectation(LOLE). Case studies in this paper demonstrates how it changes the ELCC and C.C. according to the changing of wind speed in the Tamla offshore wind farm.

A Contribution Analysis of Tamla Offshore Wind Farm by Using of Probability Simulation Method

Abstract This study aims to analyze the reliability of wind power generator in Jeju Island power system in order to maintain the operational state of power system at the reliability levels. It is also described an algorithm for probabilistic production cost credit evaluation of Wind Turbine Generators (WTG). An analysis on generated capacity of wind power generator with Tamla offshore wind farm according to two different average wind speeds such as 11.8 [m/s] and 12.8 [m/s], and without Tamla offshore wind farm have been done in terms of Effective Load Carrying Capability (ELCC) and Capacity Credit (C.C.) in power system from the perspective of Loss of Load Expectation (LOLE). According to the changing of wind speed in the Tamla offshore wind farm, this paper demonstrates how it changes the ELCC and C.C. shown as case studies.

INCORPORATION OF UNCERTAINTY IN TECHNICAL AND ECONOMIC ANALYSIS OF A FELLER-BUNCHER

The constant technical and economic analysis of timber harvesting operations is essential and determining, due to the monetary magnitude. Traditionally, these analyses are conducted deterministically, which does not allow obtaining values with probabilities of occurrence. Considering this issue, stochastic models were built in order to analyze the behavior of probabilistic production cost in felling operations with feller-buncher, through the Monte Carlo method. The study was conducted in the Central-West region of the state of São Paulo in a forest of Eucalyptus sp., with six years of age, planted in 3 x 2 m spacing. Technical analysis was based on the study of time and movements, which determined the effective productivity and economy in the hourly operating cost of the feller-buncher and in the production costs of the operation. Due to uncertainties, probability distributions were assigned to these results, which identified the most relevant variables and quantified the probabilities of the production cost. The results demonstrated that the fuel cost had a statistically significant strong positive correlation coefficient ( = 0.91) (p-value < 0.01). The hourly cost, consequently, was directly proportional to the production cost of the operation. The production cost of the operation in flat relief was 18% lower than the production cost of the operation in undulating relief.

Smarter Visual System of Generator Maintenance Scheduling Including Multi-Objective Functions by GA

Abstract: This paper proposes visualization system of generator maintenance scheduling considering various kinds of objective functions. The various kinds of objective functions include maximizing the reserve rate, maximizing probabilistic reliability and minimizing probabilistic production cost, minimizing CO 2 air pollution and maximizing satisfaction level of decision maker using fuzzy set theory. The probabilistic reliability objective includes LOLE (Loss of load expectation), EENS (Expected energy not served) and EIR(Energy index of reliability). Visualization of generator maintenance scheduling simulation systems is to compare each of the reliability, production cost and environmental according to each objective function. The practicality and effectiveness of the proposed approach are demonstrated in the simulation for a real-size power system model in Korea South-East Power CO. (KOSEP).

The Economic Viability of Renewable Portfolio Standard Support for Offshore Wind Farm Projects in Korea

Offshore wind farm (WF) projects have been promoted by support schemes as part of the expansion of renewable energy resources in Korea. This paper examines in detail how the Renewable Portfolio Standard (RPS), which was adopted post the Feed-in-Tariff scheme in 2012, has had a profound impact on the economic benefits of offshore WFs in Korea. A framework for analyzing the economic viability of RPS is presented and applied to the sixth basic plan for long-term electricity supply and demand in Korea. The electricity market price is forecast using a reformulated probabilistic production cost (PPC) model, and the renewable energy certificate (REC) price is calculated using its determination rule. The results show that the existing RPS will be ineffective in increasing the penetration of offshore WFs in Korea; however, they also indicate that the economic viability of offshore WFs could be improved by adjusting the existing RPS.

Assessing demand response and smart metering impacts on long-term electricity market prices and system reliability

This paper proposes a long-term electricity market analysis framework to assess the impacts of the demand response and smart metering infrastructure implementation on market price fluctuations and system reliability. Based on the probabilistic production cost simulation method that has been widely used in conventional power system planning, the suggested framework considers the uncertainties of the demand and the generator availability in a probabilistic manner. Furthermore, the framework considers the strategic interactions between generators (or, equivalently generation companies) and incorporates price responsive demand enabled with smart metering. To demonstrate how market equilibrium price changes and the system reliability enhances as the demand response with smart metering increases, the framework is applied to an experimental system based on the IEEE 1996 Reliability Test System (RTS) data, followed by a case study of 2010 Korean electricity markets.

Probabilistic Reliability Evaluation of the FACTS Devices Applied Power Systems

The requirement of the probabilistic approach on electric power systems is increasing on the electricity power market with various demanding of electricity power consumers. Moreover the necessity of the reliability evaluation at demand points get higher. To evaluate the reliabilities at demand points the effective load duration curve on the composite power system level (called CMELDC) was developed[1]. As the effective load duration curve plays an important role in probabilistic production cost simulation and reliability evaluation for power system planning, the CMELDC give us useful information to solve nodal probabilistic problems[2]. Two elements are required to construct the CMELDC. The first of it is the probabilistic load model. And the other is the probability of load curtailment model. This paper uses the probabilistic arrival power concept to evaluate reliability at load points. And effect of FACTS devices are presented on the probabilistic reliability.

Generation Investment and Risks Modeling in a Deregulated Electricity Market

A new probabilistic model to simulate generation investment and risks applicable in a deregulated market is proposed. In this process, probabilistic production cost simulation is applied to simulate energy dispatches. Electricity prices are simulated from a modified Black-Scholes-Merton (BSM) based price simulation model. In this work, the existing BSM model is enhanced thus making it possible to simulate the demand and supply conditions in price determination. A complete investment analysis model is then developed by integrating the above two simulation processes. Using this model, generator investors can analyze the impacts on the anticipated revenue due to different plant efficiency, availabilities, bilateral contract markets and changes in demand and supply conditions. Simulated results show that the proposed model is able to analyse the viability in generation investment with much simplicity.

Flexible Maintenance Scheduling of Generation System by Multi-Probabilistic Reliability Criterion in Korea Power System

A new technique using a search method which is based on fuzzy multi-criteria function is proposed for GMS(generator maintenance scheduling) in order to consider multi-objective function. Not only minimization of probabilistic production cost but also maximization of system reliability level are considered for fuzzy multi-criteria function. To obtain an optimal solution for generator main- tenance scheduling under fuzzy environment, fuzzy multi-criteria relaxation method(fuzzy search method) is used. The practicality and effectiveness of the proposed approach are demonstrated by simulation studies for a real size power system model in Korea in 2010. by fuzzy search method has been developed. It is expected that more flexible solution can be obtained because fuzzy set theory that can reflect the subjective decision of deci- sion-maker has been used in this study(7)-(11). An alterna- tive method for flexible GMS using various kinds of objec- tive functions is proposed in this paper. The effectiveness of the proposed approach are demonstrated by simulation studies of a real size power system model.

Assessment of the CO 2 Emission Considering the Generator Maintenance Scheduling

The emission can be decreased due to freedom of generator maintenance scheduling(GMS). This paper proposes assessment of emission considering generator maintenance scheduling(GMS) and evaluates effect of the GMS on emission. And also, this paper assesses the emission and the probabilistic production cost simulation of nuclear and thermal power generators considering operation of hydro and pumped generator. The minimum reliability criterion level satisfied production cost minimization function model is used in this paper. The practicality and effectiveness of the proposed approach are demonstrated by simulation studies for a real size power system in Korea in 2010.

Nodal probabilistic production cost simulation considering transmission system unavailabilty

A nodal probabilistic production cost simulation method is described for power system long-term expansion planning considering unavailability and delivery limitation constraints of the transmission system. This new nodal production cost simulation model includes capacity constraints and unavailabilities of generators as well as transmission lines. This simulation methodology comes from the nodal composite power system equivalent load duration curve (CMELDC), based on a new effective load model at load points developed by the authors. The nodal CMELDC can be obtained from convolution integral processing of the outage capacity probability distribution function of the fictitious generator and the original LDC. It is expected that the new simulation model based on the nodal CMELDC proposed here will provide solutions to many problems based on nodal and decentralised operation and control of electric power systems under a competition environment. The nodal CMELDC based on the new model at load points can extend application areas of nodal probabilistic production cost simulation, probabilistic congestion cost assessment, analytical outage cost assessment and nodal reliability evaluation and so on at load points. The characteristics and effectiveness of this new proposed methodology are illustrated by a small system case study using a network flow and enumeration method.

Development of a nodal effective load model considering transmission system element unavailabilities

The paper illustrates an extended effective load model including the capacities and uncertainties of generators and transmission lines. The conventional effective load model only considers the uncertainties of generators. An extended ELDC (effective load duration curve) based on the effective load model is proposed in the paper. The proposed nodal ELDC is able to supply information on nodal indices for probabilistic production cost simulation and reliability evaluation at the load points in a power system. It is expected that the effective load model presented in the paper will prove useful in many problems dealing with nodal and decentralised operation and control of electric power systems in the competitive electricity market environment. The proposed ELDC will extend the application areas of nodal probabilistic production cost simulation, outage cost assessment and reliability evaluation etc. at load points. The characteristics and effectiveness of this new effective load model are illustrated using case studies of test systems.

Distributed Utility Planning Using Probabilistic Production Costing and Generalized Benders Decomposition

Regulatory changes and advances in distributed resources (DR) technology have lead utilities to consider DRs as altematives to central station generation and T&D investments. This paper presents a comprehensive planning and production simulation model that simultaneously evaluates central and local investments to determine the optimal mix for long-term expansion. The model can also be viewed as optimizing DRs while simulating a perfectly competitive wholesale power market. The model is a mixed integer linear stochastic program that enforces Kirchhoff's current and voltage laws, and is solved using generalized Benders decomposition (GBD). The formulation includes multiarea probabilistic production costing as a subproblem. DRs and local distribution reinforcements are modeled as integer variables, while transmission and central generation options are represented as continuous variables. The model is applied to a ten-year multi-area example that suggests that DRs are able to modify capacity additions and production costs by changing demand and power flows.

Linearized segmentation method for probabilistic production cost simulation

Linearized segmentation method for probabilistic production cost simulation

Linearized segmentation method for probabilistic production cost simulation

The segmentation method is one of the basic algorithms for probabilistic production cost simulation. If the capacity block sizes are divisible by the segment size, it gives the exact expected energy and reliability values. In case a block size is not divisible by the segment size, the block size and the forced outage rate are suitably modified in the practice. However, this produces a loss in accuracy. In this paper, another approach for handling blocks with not divisible capacity sizes is presented. The basic idea is to divide each segment into two parts, temporarily, to perform the actual convolution or deconvolution computations. The zeroth and first moments of the parts are estimated based on a linear approximation. By our computational experience, at the expense of a slight increase of computational time, the accuracy can be significantly improved by the new method.

Multi-period probabilistic production cost model including dispatch constraints

This paper presents a multi-period probabilistic production cost model. Dispatch intra-period and inter-period constraints are considered through the use of a facet LP formulation. An efficient solution procedure based on the Dantzig-Wolfe decomposition technique is developed. The work reported in this paper extends previous work in two respects: (i) inter-period constraints are rigorously treated, and (ii) a systematic general solution procedure is developed. Results for a large-scale case study are presented.

Probabilistic production costing of diesel-wind energy conversion systems

This paper describes the development of a general probabilistic model of a diesel-wind energy conversion system (DWECS) composed of several diesel units, several wind turbines (wind farm), and battery storage feeding a load. The model allows the simulation of a diesel system with a wind farm of different wind turbine types considering system stability, and outages due to hardware failure and primary energy fluctuations. It is based on a modification of the convolution method, which considers a given penetration level selected by the utility for stability consideration. The production costs of the diesel units are then deduced from the expected energy not supplied (EENS) using a unit de-convolution in reverse economic order. A methodology is also presented to determine the size of the battery storage based on the excess wind energy available during operation, or that disconnected for stability consideration, while accounting for the charging/discharging cycles.

Concise method for evaluating the probability distribution of the marginal cost of power generation

In the developing electricity market, many questions on electricity pricing and the risk modelling of forward contracts require the evaluation of the expected value and probability distribution of the short-run marginal cost of power generation at any given time. A concise forecasting method is possible, which is consistent with the definitions of marginal costs and the techniques of probabilistic production costing. The method embodies clear physical concepts, so that it can be easily understood theoretically and computationally realised. A numerical example has been used to test the proposed method.

A chronological probabilistic approach in production costing with multi-area transfer consideration

In this paper, a new cumulant chronological probabilistic production costing approach for multi-area power systems is developed. The proposed approach uses an improved cumulant method, which gives an approximation of the distribution function of available capacity with the chronological load curve to estimate the expected production cost. The chronological information is taken into account in the probabilistic production costing. By this model, a gradient projection method is then used to optimize the expected production cost of the multi-area power system, constrained by the transfer capability of interconnection links. A 3-area simulation system study is presented to illustrate the applicability and efficiency of this approach.