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The integration of wind power requires the power system to be sufficiently flexible to accommodate its forecast errors. In the market clearing process, the scheduling of flexibility relies on the manner in which the wind power uncertainty is addressed in the unit commitment (UC) model. This paper presents a novel risk-based day-ahead unit commitment (RUC) model that considers the risks of the loss of load, wind curtailment and branch overflow caused by wind power uncertainty. These risks are formulated in detail using the probabilistic distributions of wind power probabilistic forecast and are considered in both the objective functions and the constraints. The RUC model is shown to be convex and is transformed into a mixed integer linear programming (MILP) problem using relaxation and piecewise linearization. The proposed RUC model is tested using a three-bus system and an IEEE RTS79 system with wind power integration. The results show that the model can dynamically schedule the spinning reserves and hold the transmission capacity margins according to the uncertainty of the wind power. A comparison between the results of the RUC, a deterministic UC and two scenario-based UC models shows that the risk modeling facilitates a strategic market clearing procedure with a reasonable computational expense. [ABSTRACT FROM PUBLISHER]

In a carbon-constrained world, CO2 emissions will become a new concern in power system dispatch. Meanwhile, carbon capture power plants (CCPPs), which are a critical low-carbon power generation option, will have a significant impact on power system operation and dispatch. This paper presents research on low-carbon power system dispatch (LCPSD) incorporating CCPPs. The operating mechanism of CCPPs is investigated first. Then, the operating characteristics of CCPPs in power system dispatch are analyzed, including feasible power output limits, ramping rates and relationships between the power outputs and the carbon emissions. A comprehensive LCPSD model is formulated, in which the carbon emissions of power plants are treated as a new set of decision variables, and low-carbon-related cost terms are considered. The dispatch features of CCPPs are elaborately formulated and incorporated into the LCPSD model. The effectiveness and the validity of the proposed LCPSD mode and model are demonstrated using numerical examples based on an IEEE 118-bus tested system. [ABSTRACT FROM PUBLISHER]

Discrete convolution (DC) is a generally accepted approach for the probabilistic analysis such as reliability assessment and probabilistic load flow. However, it has a strong precondition that the stochastic variables being convolved must be independent, which may not be fully satisfied in all cases. Using copula functions, this letter derives the formulation of DC for dependent variables. The performance of the proposed dependent discrete convolution (DDC) is illustrated using reliability assessment involving wind power. The result shows that the DDC inherits the efficient and reliable performance of DC, indicating a promising potential for practical applications. [ABSTRACT FROM PUBLISHER]

A foreword to the March 15, 2015 issue of "IEEE Transactions on Power Systems" is presented.

Composite power system reliability evaluation is computationally time-consuming because the optimal power flow (OPF) with the least load curtailment is calculated for a large number of samples. Most current studies of probabilistic simulation methods focus on sampling techniques to improve the sampling efficiency and decrease the calculations of the OPF. This paper proposes a fast reliability evaluation method that accelerates the calculation of the OPF using multi-parametric linear programming (MPLP). In this paper, the sampled transmission line statuses are considered by the transmission line status dictionary (TLSD). We match the line status of each sample with the scenarios in the TLSD. For matching samples, the generation status and the sampled load are treated as MPLP parameters of the DC-OPF evaluation model. A dynamic learning algorithm is applied to solve the MPLP problem. Case studies are conducted on both IEEE Reliability Test Systems and a provincial power system in China. The results show that the proposed method improves the evaluation efficiency by 23–30 times compared with the normal DC-OPF-based model. [ABSTRACT FROM AUTHOR]

The integration of high penetration of renewable energy brings greater uncertainties for the operation of future power systems due to its intermittency and lack of predictability. The uncertainties brought by wide scale renewables might have dependencies with each other because their outputs are mainly influenced by weather. However, an analysis of such uncertainties with complex dependencies faces the “curse of dimensionality”. This challenges the power system uncertainty analysis in probabilistic forecasting, power system operation optimization, and power system planning. This paper proposes an efficient approach that is able to handle high-dimensional dependencies. The approach uses the high-dimensional Copula theory and discrete convolution method to conduct a high-dimensional dependent discrete convolution (DDC) calculation. A recursive algorithm is proposed to decompose the computation of DDC into multiple convolutions between each pair of stochastic variables so that the “curse of dimensionality” is solved. The computational complexity of the proposed method is linear with respect to the number of dimensions and guarantees computational efficiency. Finally, illustrative examples of power system reserve requirement evaluation and wind power capacity credit assessment analysis are used to verify the effectiveness and superiority of the proposed approach. [ABSTRACT FROM PUBLISHER]

Short-term load forecasting (STLF) is the basis of power system planning and operation. With regard to the fast-growing load in China, a novel two-stage hybrid forecasting method is proposed in this paper. In the first stage, daily load is forecasted by time-series methods; in the second stage, the deviation caused by time-series methods is forecasted considering the impact of relative factors, and then is added to the result of the first stage. Different from other conventional methods, this paper does an in-depth analysis on the impact of relative factors on the deviation between actual load and the forecasting result of traditional time-series methods. On the basis of this analysis, an adaptive algorithm is proposed to perform the second stage which can be used to choose the most appropriate algorithm among linear regression, quadratic programming, and support vector machine (SVM) according to the characteristic of historical data. These ideas make the forecasting procedure more accurate, adaptive, and effective, comparing with SVM and other prevalent methods. The effectiveness has been demonstrated by the experiments and practical application in China. [ABSTRACT FROM PUBLISHER]

With substantially increasing penetration levels of wind power, electric power system flexibility is needed to address the variability and uncertainty of wind power output. Thus, it has become an urgent issue to obtain an optimal tradeoff between economics and reliability, and to price system uncertainties. This paper proposes a new electricity market-clearing mechanism based on locational marginal prices (LMPs) for pricing uncertain generation and load. The uncertainty contained locational marginal price (U-LMP) is derived from a distributionally robust chance-constrained optimal power flow model in which only the first-order and second-order moments of the uncertain sources’ probability distribution are needed. Compared with traditional LMPs, the proposed U-LMP formulation includes two new uncertainty components: transmission line overload uncertainty price and generation violation uncertainty price. These LMP uncertainty components are the price signals reflecting the system costs as a result of wind generation and demand uncertainty at different locations. Finally, using parametric case studies, the relationship among uncertainty levels, system generation cost, and LMP uncertainty components are established. Case studies performed on the PJM 5-bus and IEEE 118-bus systems verify the proposed U-LMP method. [ABSTRACT FROM AUTHOR]

The integration of variable renewable energy (VRE) generation, i.e., wind power and solar photovoltaic, brings significant uncertainty for the power system operation. Different with VRE techniques, concentrating solar power (CSP) is an appealing renewable generation technology due to its dispatch ability through the use of thermal energy storage and is thus expected to play a significant role in high renewable energy penetrated power systems. In this paper, we propose a look-ahead stochastic unit commitment model to operate power systems with CSP under high renewable energy penetration. It has a three-stage structure. The first stage optimizes the operational decisions in a day-ahead framework based on forecasts; the second stage minimizes the expected generation cost for possible realizations in the real time; and the third stage accounts for look-ahead operation in future operating days. This paper has a dual purpose: first, exploring how CSP plants operate in high renewable penetrated power systems; and second, analyzing the benefits of CSP in accommodating VRE generation. A case study on a modified IEEE RTS-79 system with actual solar and wind power data is provided to validate the proposed method. [ABSTRACT FROM AUTHOR]

Achieving high renewable energy penetrated power systems requires considerable operational flexibility to hedge the variability and uncertainty of variable renewable energy (VRE) generation. Compared with VRE sources, concentrating solar power (CSP) is an emerging controllable renewable generation technique that utilizes solar thermal power to generate electricity. The operational dispatchability of CSP would contribute to the power system transition toward high renewable penetration. In this paper, we explore how the generation portfolio will change toward high renewable energy penetrations, how much cost is involved, and what role CSP will play in realizing a high renewable energy penetrated power system. This study relies on a stochastic two-stage generation and transmission expansion planning model with CSP plants. The model captures the uncertainty and variability of renewable generation and the flexibility limits of thermal plants. With the target of achieving a renewable-dominated minimum-cost system with an expected renewable energy penetration level, the investments of both generation and transmission facilities are optimized. A case study on IEEE test systems with renewable technology cost data in 2050 is performed to analyze the value of CSP toward high renewable energy penetrated power systems. [ABSTRACT FROM AUTHOR]

This paper introduces a dynamic multiplier-based Lagrangian relaxation approach for the solution to multi-area economic dispatch (MAED) in a fully decentralized manner. Dynamic multipliers refer to the multipliers associated with power balance equations at tie-line buses in each area. Dynamic multipliers can be approximated as linear functions of tie-line power exports via sensitivity analysis and can serve as the equivalent supply/demand functions to neighboring areas. In contrast to the conventional static point-wise multiplier, which is unable to reflect the marginal cost change that results from variations in the power exchange level, the proposed dynamic multiplier provides each area the look-ahead capability to foresee the range of the marginal cost for power export over a range of tie-line exchange variations. In turn, this allows for a significantly faster convergence to the global optimal solution. The algorithm is also shown to be early termination friendly, which is very desirable in practice for ultra-large systems such as the State Grid of China. Numerical examples in a 6-bus system, a 3-area 354-bus IEEE system, and large test systems illustrate the benefits of the proposed algorithm. [ABSTRACT FROM PUBLISHER]