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The papers in this special section examine the enabling of very high penetration renewable energy integration into future power systems. Renewable energy is regarded as one of the main solutions to achieve power system sustainability. In recent years, renewable energy has been experiencing fast development. In 2016, 24.5% of the total power demand in the world was supplied by renewable energy, where 5.5% was produced by wind power and PV [item 1) in the Appendix]. It is estimated that by the end of 2017 the global wind power and PV capacity had reached about 565 GW and 335 GW respectively. Some power systems have already entered the era of high penetration of intermittent renewable energy integration. [ABSTRACT FROM PUBLISHER]

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]

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]

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]