Search

As a clean fuel source, natural gas plays an important role in achieving a low-carbon economy in the power industry. Owing to the uncertainties introduced by increasing utilization of natural gas in electric power system, gas system and electricity system should be planned in an integrated manner. When considering these two systems simultaneously, there are many emerging difficulties, e.g., increased system complexity and risk, market timeline mismatch, overall system reliability evaluation, etc. In this paper, a novel expansion co-planning (ECP) framework is proposed to address the above challenges. In our approach, the planning process is modeled as a mixed integer nonlinear optimization problem. The best augmentation option is a plan with the highest cost/benefit ratio. Benefits of expansion planning considered are reductions in operation cost, carbon emission cost, and unreliability cost. By identifying several scenarios based on statistical analysis and expert knowledge, decision analysis is used to tackle market uncertainties. The operational and economic interdependency of both systems are well analyzed. Case studies on a three-bus gas and two-bus power system, plus the Victorian integrated gas and electricity system in Australia are presented to validate the performance of the proposed framework. [ABSTRACT FROM PUBLISHER]

Distributed generation (DG) has been rapidly integrated into microgrids. However, uncertain power generation of intermittent DG such as wind turbines brings challenges to the DG planning problem. This paper proposes a novel probability-weighted robust optimization (PRO) method to allocate DG units including microturbines and wind turbines in microgrids, aiming to maximize the total profit over a long-term planning horizon. First, probability-weighted uncertainty sets are proposed to model uncertainties including wind turbine output and load demand during a long-term planning horizon which can fully cover the uncertainty spectrum and accurately represent the uncertainty probability distribution. Then, the PRO method optimizes DG sizing and locating under the worst uncertainty cases considering their occurrence probabilities. Therefore, the planning decision is robust against any possible uncertainty realization. Besides, a modified column-and-constraint generation algorithm is developed to solve the PRO problem. Simulation results show that the DG planning obtained by the proposed method can achieve full operating robustness. [ABSTRACT FROM AUTHOR]

High-level wind power integration can dramatically affect a power system's dynamic performance and introduce significant uncertainties to system's operation. This paper proposes a robust dispatch method to optimize the power system's operation state while sustaining its transient stability with highly variable and stochastic wind power generation. The problem is first modeled as an augmented optimal power flow model with uncertain variables and differential-algebraic equations. Then, the stability constraints are converted to approximately-equivalent algebraic equations based on one-machine-infinite-bus equivalence technique and trajectory sensitivity analysis. Next, the uncertain wind power generation is represented by a small number of strategically selected testing scenarios. Finally, a decomposition-based computation strategy is developed to divide the original model into a master problem and a series of slave problems which are solved iteratively. Using industry-grade system dynamic models and simulation software, the proposed method is tested on the New England 39-bus system and Nordic32 system, showing high performance on economic optimality, stability robustness, and computational efficiency. [ABSTRACT FROM PUBLISHER]

In the smart grid environment, a power system restoration plan with flexibility is needed to improve reliability and efficiency in an automated way. Traditional system restoration methods based on fixed time intervals may not meet the requirement. This article proposes a new permutation-based model for power system restoration within an optimized flexible duration considering available generator capability and load prioritization. By utilizing this new model, power system restoration is formulated as a permutation-based combinatorial optimization problem to maximize the restored load per unit time. Finally an action-by-action flexible time schedule is obtained for generator startup. A novel quantum-inspired evolutionary algorithm, called the quantum-inspired differential evolutionary algorithm, has been applied to solve this problem due its to high population diversity and fast convergence. The effectiveness of the proposed restoration approach has been validated using IEEE 39- and 57-bus systems. [ABSTRACT FROM PUBLISHER]

Highlights • A two-layer collaborative planning for inverter interfaced DGs and BES units is established for enhanced voltage regulation functions in ADN. • The potential of DG inverter over-sizing design at planning stage, the fully utilization mode of reactive power output of DG inverters at operation stage are discussed. • A time-varying ZIP load model is adopted representing the dynamic load characteristics. • The uncertainties of the DG power output are modeled based on the Taguchi robust test design approach. Abstract Active Distribution Networks (ADNs) are featured by large-scale integration of distributed generation (DG) and energy storage. This paper proposes a novel two-layer co-planning method for optimal placement of inverter-interfaced DG and battery energy storage (BES) units towards enhanced voltage regulation functions in an ADN. The outer-later model determines the planning decision of DG units and the inverter sizing, location, and capacities of BES units, respectively; the inner-layer model corresponds to the operation decision which aims to optimally schedule the BES's charging/discharging and reactive power from inverters for voltage regulation support considering the conservation voltage reduction (CVR). The Taguchi's Orthogonal Array Testing (TOAT) is used to select a small number of scenarios to represent the DG power output uncertainties. The load is represented by a time-varying ZIP load model. The proposed model is tested on a modified IEEE 33-bus radial distribution system and the results to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

As an important way of addressing energy and environmental challenges, the market share of wind power generation has increased dramatically in the past decade and has introduced significant challenges to power system operation. In this article, the tail correlation between multiple wind farms is studied. The joint probability distribution of multiple wind farms is estimated by employing the Gumbel copula function. Based on the estimated joint probability distribution, a stochastic optimal dispatch model is proposed to take into account the chance constraints of energy utilization from multiple wind farms in the power system. The sample average approximation method is employed to handle the chance constraints in the proposed model, so as to transform stochastic optimal dispatch into a deterministic non-linear optimization problem. The quantum-inspired evolutionary algorithm is used to solve the proposed model. The proposed model and algorithm are tested with comprehensive case studies to demonstrate their effectiveness. [ABSTRACT FROM AUTHOR]

Levelized cost of electricity (LCOE) is widely used to compare the cost of different electricity generation technologies. However, with the increasing penetration of variable renewable energy (VRE), it is inappropriate to use traditional equations to calculate the LCOE for non-dispatchable VRE due to its intermittent nature. Therefore, this paper reviews LCOE for renewable generation considering aspects such as improving traditional methods for evaluating and reducing the LCOE of VRE. The existing methods for improving the accuracy of the traditional LCOE have been divided into four aspects: investment-related cost, operating-related cost, expressions for plant performance and the uncertainty and risk-related costs. The review summarizes the existing studies from academic articles and technical reports and proposes suggestions for future research studies. • Comprehensive Levelized cost of electricity (LCOE) methods review. • LCOE evaluation from cost, plant performance, uncertainty and risk. • Summary of existing methods for improving the LCOE estimation accuracy of variable renewable energy (VRE). • Compare and summarize the existing VRE LCOE methods in academic papers and technical reports. [ABSTRACT FROM AUTHOR]