Control of MMC-based STATCOM as an effective interface between energy sources and the power grid

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This paper presents a dynamic model of modular multilevel converters (MMCs), which are considered as an effective interface between energy sources and the power grid. By improving the converter performance, appropriate reactive power compensation is guaranteed. Modulation indices are calculated based on detailed harmonic evaluations of both dynamic and steady-state operation modes, which is considered as the main contribution of this paper in comparison with other methods. As another novelty of this paper, circulating current control is accomplished by embedding an additional second harmonic component in the modulation process. The proposed control method leads to an effective reduction in capacitor voltage fluctuation and losses. Finally, converter’s maximum stable operation range is modified, which provides efficiency enhancements and also stability assurance. The proficiency and functionality of the proposed controller are demonstrated through detailed theoretical analysis and simulations with MATLAB/Simulink

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

Pave the way for sustainable smart homes: a reliable hybrid AC/DC electricity infrastructure

The development of emerging smart grid technologies has led to more and more penetration of renewable energy resources and electric energy storage in the residential sectors. Besides, owing to the significant evolution of power electronic devices, there is a rapid growth in penetration of DC loads and generations, such as PV and electric vehicles (EVs), into the buildings and homes as a building block of the future smart cities. This is despite the fact that the electricity infrastructure of the conventional buildings is designed based on AC electricity and as a result, there would be a lot of losses due to the frequent power conversion from AC to DC and vice versa. Besides, according to a significant amount of energy consumption in the residential sector, buildings have a prominent role to confront environmental problems and obtain sustainability. In such circumstances, and considering the energy outlook, rethinking the electrification structure of the built environment is necessary. This work is an effort in this regard and looks for a sustainable energy infrastructure for the cyber–physical homes of the future. Three disparate electrification architectures are analyzed. The proposed framework, which is formulated as a mixed-integer linear programming (MILP) problem, not only considers costs associated with investment and operation but also evaluates the reliability of each structure by considering the different ratios of DC loads. Moreover, the optimal size of renewable energy resources and the effect of EV demand response, and different prices of PV and battery are precisely investigated. The efficacy of the proposed approach is evaluated via numerical simulation

Price forecasting of electricity markets in the presence of a high penetration of wind power generators

© 2017 by the authors. Price forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price is influenced by wind energy; therefore, price forecasting can be more complicated. This paper proposes a novel hybrid approach for price forecasting of day-ahead markets, with high penetration of wind generators based on Wavelet transform, bivariate Auto-Regressive Integrated Moving Average (ARIMA) method and Radial Basis Function Neural Network (RBFN). To this end, a weighted time series for wind dominated power systems is calculated and added to a bivariate ARIMA model along with the price time series. Moreover, RBFN is applied as a tool to correct the estimation error, and particle swarm optimization (PSO) is used to optimize the structure and adapt the RBFN to the particular training set. This method is evaluated on the Spanish electricity market, which shows the efficiency of this approach. This method has less error compared with other methods especially when it considers the effects of large-scale wind generators

Holistic approach to resilient electrical energy distribution network planning

This paper proposes a two-objective linearized resilient architecture (LRA) model for distribution networks to achieve a strictly resilient network during natural disasters like earthquakes and floods. To obtain this goal, the proposed LRA framework is based on the planning of the energy storage system (ESS), hardening and tie lines, and backup distributed generation (DG). Therefore, the proposed model minimizes the sum of planning and expected operation costs in the first objective function, and the total load shedding and repair costs originates from earthquakes and floods in the second objective function. Also, it constraints to the network planning model, linearized equations of the system operation, and system reconfiguration formulation. Moreover, stochastic programming models the uncertain availability of the network equipment during the natural disaster condition, the load and electricity price. In the next step, the ε-constraint-based Pareto optimization is used to achieve an equivalent single-objective LRA model and obtain the best compromise solution. Finally, the proposed strategy is applied to a standard test distribution network. Numerical simulation confirms the capability of the proposed method in obtaining a resilient distribution network during natural disasters

Application of small-scale compressed air energy storage in the daily operation of an active distribution system

While compressed air energy storage (CAES) has many applications in the field of generation and transmission power systems based on the state-of-the-art, this paper proposes the application of small-scale CAESs (SCAESs) in form of a storage aggregator in the daily operation of an active distribution system (ADS), joining the distribution system operator (DSO) for the participation in the day-ahead (DA) wholesale market. An innovative two-agent modeling approach is formulated. The first agent is responsible for aggregating SCAES units and the profit maximization of the aggregator is based on the distribution local marginal price. The DSO as the second agent receives the DA scheduling from the independent SCAES aggregator and is thus responsible for the secure operation of the ADS, utilizing solar and dispatchable distributed generation (DG) as well as purchasing power from the wholesale market. Linear programming is used for the formulation and optimization of the SCAES aggregator, while a bi-objective optimization algorithm (with the objectives of minimum operating cost as well as minimum power loss and emissions in different scenarios) is employed for DSO scheduling. The results show that the CAES aggregator can offer a considerable impact for power loss reduction, specifically, when diesel generators are not committed in the system operation (i.e., where emission has very low values between 10,000 and 12000 kg). Additionally, the CAES aggregator could reduce the operation costs of the grid in a wide range of operations, even though for the scenario in which the CAES units are not under the control of the DSO anymore and also are scheduled to maximize their own profit. Moreover, results demonstrated that CAES units can be a significant voltage control device for a distribution grid with different objectives. Finally, some conclusions are duly drawn