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This paper proposes a multi-timescale coordinated stochastic voltage/var control method for high renewable-penetrated distribution networks. It aims to utilize multiple devices to counteract uncertain voltage fluctuation and deviation. In the hourly timescale (first stage), capacitor banks and transformer tap changers are scheduled before stochastic renewable output and load variations are realized. In the 15-min timescale (second stage), inverters that interface the renewable energy resources provide var support to supplement the first-stage decision after uncertainty is observed. The coordination is model as a two-stage stochastic programming problem with scenario reduction. It is then converted to a deterministic mixed-integer quadratic programming equivalence model and solved by commercial solvers combined. Compared with existing methods, the proposed volt/var control can achieve lower expected energy loss and can sustain a secure voltage level under random load demand and renewable power injection. The proposed method is verified on the IEEE 33-bus distribution network and compared with existing practices. [ABSTRACT FROM PUBLISHER]

Natural gas is an important fuel source in the power industry. Electricity and natural gas are both energy that can be directly consumed. To improve the overall efficiency of the energy infrastructure, it is imperative that the expansion of gas power plants, electricity transmission lines and gas pipelines can be co-planned. The co-planning process is modeled as a mixed integer nonlinear programming problem to handle conflicting objectives simultaneously. We propose a novel model to identify the optimal co-expansion plan in terms of social welfare. To evaluate the robustness of plans under different scenarios, the flexibility criterion is used to identify each plan’s adaptation cost to uncertainties, such as demand growth, fuel cost and financial constraints, etc. We developed a systematic and comprehensive planning model to understand, develop and optimize energy grids in order to reach higher social welfare, and is therefore of great importance in terms of supporting and guiding investment decisions for the power and gas industry. Meanwhile, we use the sequential importance sampling (SIS) to perform scenario reduction for achieving a higher computational efficiency. A comprehensive case study on the integrated IEEE 14-bus and a test gas system is conducted to validate our approach. [ABSTRACT FROM PUBLISHER]

Distribution system operators aim to improve hosting capacity (HC) of distribution networks (DNs) to accommodate more distributed rooftop photovoltaics (PVs). Although PV power generation delivers numerous benefits, power unbalance and voltage rise are two major obstacles that limit the network HC. To mitigate these issues, battery energy storage systems (BESSs) can be applied. Thus, this paper proposes a robustly optimal allocation method for BESSs, which aims to reduce the power unbalance and alleviate the voltage rise, and thus improve the HC of the unbalanced three-phase DNs. Considering that locations and capacities of distributed rooftop PVs are determined by customers, future PV installations are regarded as uncertainties. In addition, to deal with the uncertainties, the proposed BESS allocation problem is formulated as an adaptive robust optimization (ARO) model with integer recourse variables. Accordingly, a solution algorithm which integrates an alternating optimization procedure into a column-and-constraint generation algorithm is developed to efficiently solve the ARO model. With the proposed BESS allocation method, a new perspective on HC improvement is provided, which not only considers the worst power unbalance situation but also satisfies the allowed maximum PV capacity. The simulation results verify high efficiency and solution robustness of the proposed allocation method. [ABSTRACT FROM AUTHOR]

Power grid resilience, reliability, and sustainability can be improved by decomposing the large-scale grids into the Networked Microgrids (NMGs). However, different MGs may have different roles and policies. Hence, in comparison with conventional networks, optimal energy management, as well as the grid reconfiguration of the NMGs is more completed and challenging. This article develops a three-layer cloud-fog computing architecture for energy management of reconfigurable NMGs considering dynamic thermal line rating (DLR) constraint. DLR can potentially affect the ampacity of feeders, especially in the islanding mode, when lines approach their maximum capacity. In order to avoid any feeder contingency in the off-grid (islanded) mode, the reconfiguration technique as well as the cloud fogs framework are employed to change the topology of the NMG network swiftly and release the line capacity. Finally, the proposed problem is formulated as a mixed-integer linear optimization problem considering DLR constraint. The proposed model is examined on a modified IEEE 69 bus test network. Results demonstrate the high performance and effectiveness of the developed model and also validate its reliability and economic assets. [ABSTRACT FROM AUTHOR]

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]

Energy sharing has the potential to decrease operation costs by exploiting the complementarity of different entities. The further increase of microgrids’ generation capacity in the distribution network could trigger the emergence of MW-level microgrids aggregator (MGA) to participate in the regional energy sharing market (ESM) and sell excess energy. However, the uncertainty of renewable energy generation will cause the failure of energy sharing and an economic loss for the energy sharing market. Therefore, a novel two-sided market with the clearing price and risk management is proposed in this paper. In order to optimize the energy sharing strategy of MGAs, a network-constrained risk-averse two-stage stochastic game model based on DC optimal power flow (OPF) is established. The objective of the MGA is to maximize the revenue in the ESM while the subtransmission network operator (SNO) aims to achieve regional energy self-sufficiency. The sample average approximation (SAA) method is employed to approximate the stochastic Cournot-Nash equilibrium. A distributed market clearing algorithm based on the alternating direction method of multipliers (ADMM) and best response seeking is developed to solve the optimal bidding problem of MGAs and the optimal dispatch problem of the SNO. Furthermore, the existence of the SAA Nash equilibrium is also investigated. Numerical simulations prove that the proposed game model can effectively control the overbidding risk and promote regional energy self-sufficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a robust optimization approach for optimal operation of microgrids. The uncertain output variation of renewable energy sources (RESs) is addressed by collaboratively scheduling of energy storage (ES) and direct load control (DLC) through a two-stage complementary framework: an hour-ahead charging/discharging of ES and a quarter-hour-ahead activation of DLC. The objective is to maximize the total profit of the microgrid considering operation and maintenance costs of ES units, wind turbines and photovoltaics, and transaction with main grid and customer loads. Assuming the power output of RES randomly varies within a bounded uncertainty set, the problem is modeled to a two-stage robust optimization model and solved by a column-and-constraint generation algorithm. Compared with conventional operation methods, the ES and DLC are coordinated in different time-scales, and RES uncertainties are fully addressed during operation decision-making, ensuring the solutions to be optimal and robust for any realization of uncertainty. The proposed methodology is verified on the IEEE 33-bus distribution system through a wide range of different tests. [ABSTRACT FROM AUTHOR]

The restructuring of global power industries has introduced a number of challenges, such as conflicting planning objectives and increasing uncertainties, to transmission network planners. During the recent past, a number of distributed generation technologies also reached a stage allowing large-scale implementation, which will profoundly influence the power industry, as well as the practice of transmission network expansion. In the new market environment, new approaches are needed to meet the above challenges. In this paper, a market simulation-based method is employed to assess the economical attractiveness of different generation technologies, based on which future scenarios of generation expansion can be formed. A multi-objective optimization model for transmission expansion planning is then presented. A novel approach is proposed to select transmission expansion plans that are flexible given the uncertainties of generation expansion, system load, and other market variables. Comprehensive case studies will be conducted to investigate the performance of our approach. In addition, the proposed method will be employed to study the impacts of distributed generation on transmission expansion planning. [ABSTRACT FROM PUBLISHER]

The predispatch price forecast plays a key element in the electricity market. However, such a forecast usually depends on the traditional offline batch-learning technologies, which cannot respond in time to the unexpected changes in the local power system environment. Further, the predispatch local price forecast is often affected by the dynamic price changes from the neighboring regions. This article proposes a novel online learning forecast approach to overcome the above issues to provide a better predispatch price forecast by using the online sequential extreme learning machine (OS-ELM) algorithm. The article proposes a novel data structure in the form of a 2-D orthogonal list and two corresponding OS-ELM modules. One module provides the rolling day-ahead price prediction and prediction intervals using the day-by-day online training update, while the other provides the rolling 30-min prediction using the 2-h-by-2-h online training update. The proposed approach can continuously perceive any unexpected events and any price fluctuations from the neighboring regions in the nonlinear patterns. The proposed approach is validated using simulation studies based on the data from the Australian electricity market, and the simulation results show that the proposed approach can help in improving the forecast accuracy, especially when unexpected changes occur both locally and in the neighboring area. [ABSTRACT FROM AUTHOR]

This paper proposes a coordinated multilayer control strategy for the energy management (EM) of grid-connected ac microgrids. The strategy predicts the customer's load demand and the photovoltaics (PV) power generation for a day-ahead EM. It utilizes the PV power generations and the bidirectional energy transactions from electric vehicles and battery storage to provide a combined response for load support. The system also predicts any uncertainties in customers demand and power generations and implements day-ahead precautionary measures to tackle that uncertainty. Two different prediction strategies are used, autoregressive moving average and artificial neural networks, and their performances for a day-ahead EM are investigated. The reference power from the tertiary layer EM is sent to the local controllers for power regulation at the inverter level. Additionally, the varying power output reference obtained from a day-ahead EM is classified into the slow, medium, and fast variations. The performance of the local controller employed in the interfacing grid-connected three-phase inverter is tested during the above-mentioned power reference variations. The total harmonic distortion incorporating a moving window technique is calculated for the ac output current during each class of power variations over a day. [ABSTRACT FROM AUTHOR]

High penetration of distributed renewable energy introduces significant uncertainties to active distribution networks. Optimal control methods accounting for inherent uncertainties are needed to facilitate economic and reliable operation of active distribution networks. This paper proposes a stochastic receding horizon control method based on modified stochastic model predictive control framework to integrate high penetration of distributed generation. Multiple controllable resources are jointly optimized over a finite prediction horizon while ensuring relevant security restrictions. The simplified Z-bus sensitivity for active distribution networks is developed for computationally efficient estimation of system nonlinearity with high accuracy, and is combined with the sequential linear programming to iteratively derive the linear state space model for compensation of cumulative modeling errors. Furthermore, the voltage limitations are reformulated as chance constraints to indicate the probabilistic reliability index of voltage qualification rate, and achieve tradeoffs between cost reduction and voltage regulation. The affine-disturbance feedback control policy is leveraged here to enforce close-loop control performance and analytically transform intractable chance constraints into second-order cone constraints. Comprehensive case studies based on 33-bus and 123-bus distribution systems are carried out to demonstrate the capability and effectiveness of the proposed approach in terms of modeling accuracy, control performance, cost reduction, and method scalability. The proposed approach can effectively enforce voltage regulation against uncertainties with the prescribed probability level. Control costs and constraint violation can be reduced compared with deterministic model predictive control and open-loop control strategies. [ABSTRACT FROM AUTHOR]

The fluctuation and uncertainty of wind power generation bring severe challenges to secure and economic operation of power systems. Because wind power forecasting error is unavoidable, probabilistic forecasting becomes critical to accurately quantifying the uncertainty involved in traditional point forecasts of wind power and to providing meaningful information to conduct risk management in power system operation. This paper proposes a novel direct quantile regression approach to efficiently generate nonparametric probabilistic forecasting of wind power generation combining extreme learning machine and quantile regression. Quantiles with different proportions can be directly produced via an innovatively formulated linear programming optimization model, without dependency on point forecasts. Multistep probabilistic forecasting of 10-min wind power is newly carried out based on real wind farm data from Bornholm Island in Denmark. The superiority of the proposed approach is verified through comparisons with other well-established benchmarks. The proposed approach forms a new artificial neural network-based nonparametric forecasting framework for wind power with high efficiency, reliability, and flexibility, which can be beneficial to various decision-making activities in power systems. [ABSTRACT FROM AUTHOR]

Accurate and reliable forecast of wind power is essential to power system operation and control. However, due to the nonstationarity of wind power series, traditional point forecasting can hardly be accurate, leading to increased uncertainties and risks for system operation. This paper proposes an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrap methods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified with the best performance. Consequently, a new method for prediction intervals formulation based on the ELM and the pairs bootstrap is developed. Wind power forecasting has been conducted in different seasons using the proposed approach with the historical wind power time series as the inputs alone. The results demonstrate that the proposed method is effective for probabilistic forecasting of wind power generation with a high potential for practical applications in power systems. [ABSTRACT FROM PUBLISHER]