Abstract
Since wind turbines or photovoltaic (PV) panels are generally connected to the power grid by power electronic inverters, the power system inertia is gradually decreasing along with the growing share of renewable energy. This jeopardizes the system frequency response dynamics so that the corresponding frequency security issue is becoming the bottle-neck factor that restricts the development of high renewable energy penetration. Consequently, power system scheduling models need to incorporate frequency dynamics. The difficulty lies in how to formulate the frequency security constraints from the perspective of hourly load-generation balance since the frequency dynamics have a shorter time scale (5 similar to 30 s). Several modeling methods have been proposed based on different assumptions and simplifications. However, their accuracy is not clear. We first propose a novel method to formulate linear frequency security constraints, which considers more details of frequency response dynamics. Then, an evaluation methodology is designed to quantify the accuracy of those frequency constraints. Using this evaluation method, we compare two typical methods in recent literature with the proposed method. The results show the effectiveness and superiority of our proposed method.

Abstract
As a response to rapidly increasing penetration of wind power generation in modern electric power grids, accurate prediction models are crucial to deal with the associated uncertainties. Due to the highly volatile and chaotic nature of wind power, employing complex intelligent prediction tools is necessary. Accordingly, this article proposes a novel improved version of empirical mode decomposition (IEMD) to decompose wind measurements. The decomposed signal is provided as input to a hybrid forecasting model built on a bagging neural network (BaNN) combined with K-means clustering. Moreover, a new intelligent optimization method named ChB-SSO is applied to automatically tune the BaNN parameters. The performance of the proposed forecasting framework is tested using different seasonal subsets of real-world wind farm case studies (Alberta and Sotavento) through a comprehensive comparative analysis against other well-known prediction strategies. Furthermore, to analyze the effectiveness of the proposed framework, different forecast horizons have been considered in different test cases. Several error assessment criteria were used and the obtained results demonstrate the superiority of the proposed method for wind forecasting compared to other methods for all test cases.

Abstract
Wind power curtailment (WPC) occurs because of the non-correlation between wind power generation (WPG) and load, and also due to the fast sub-hourly variations of WPG. Recently, advances in energy storage technologies facilitate the use of bulk energy storage units (ESUs) to provide the ramping required to respond to fast sub-hourly variations of WPGs. To minimize the sub-hourly WPC probability, this paper addresses a generic continuous-time risk-based model for sub-hourly scheduling of energy generating units and bulk ESUs in the day-ahead unit commitment (UC) problem. Accordingly, the Bernstein polynomials are hosted to model the continuous-time risk-based UC problem with ESU constraints. Also, the proposed continuous-time risk-based model ensures that the generating units and ESUs track the sub-hourly variations of WPG, while the load and generation are balanced in each sub-hourly intervals. Finally, the performance of the proposed model is demonstrated by simulating the IEEE 24-bus Reliability and Modified IEEE 118-bus test systems.

Abstract
A bi-level operation scheduling of distribution system operator (DSO) and multi-microgrids (MMGs) considering both the wholesale market and retail market is presented in this paper. To this end, the upper-level optimization problem minimizes the total costs from DSO's point of view, while the profits of microgrids (MGs) are maximized in the lower-level optimization problem. Besides, a scenario-based stochastic programming framework using the heuristic moment matching (HMM) method is developed to tackle the uncertain nature of the problem. In this regard, the HMM technique is employed to model the scenario matrix with a reduced number of scenarios, which is effectively suitable to achieve the correlations among uncertainties. In order to solve the proposed non-linear bi-level model, Karush-Kuhn-Tucker (KKT) optimality conditions and linearization techniques are employed to transform the bi-level problem into a single-level mixed-integer linear programming (MILP) optimization problem. The effectiveness of the proposed model is demonstrated on a real-test MMG system.

Abstract
Nowadays demand response (DR) is known as one of the main parts of the power system especially in the smart grid infrastructure. Furthermore, to enhance the participation of the consumers in the DR programs, the Independent System Operators (ISOs) have introduced a new entity, i.e. Demand Response Aggregator (DRA). The main contribution of this paper is to investigate a novel framework to increase the profits of the DRA participating in the day-ahead electricity market, i.e. employment of an axillary generation system in the DRA entity. It is supposed that the DRA in this paper has an axillary generation system and it would lead to an increase in the profit of the DRA through avoiding the economic loss in the process of trading DR obtained by the active participation of prosumers in the electricity market. The fuel cell is introduced as the axillary generation unit to the DRA unit. In the framework proposed in this paper, the DR is acquired from end-users during peak periods and will be offered to the day-ahead electricity market. The power flow during the off-peak hours is in another direction, i.e. from the grid to the consumers. In this model, the information-gap decision theory (IGDT) is chosen as the risk measure. The uncertain parameter is the day-ahead electricity market price. The optimization problem's objective is to maximize the profit of the DRA. The behavior of the risk-seeker decision-maker is analyzed and investigated. The feasibility of the program is demonstrated by applying it to realistic data.

Abstract
A viable option for electrification of remote areas far from power grids is to set up microgrids and feed them with local generation. Such microgrids are referred to as isolated microgrids and due to the lack of possibility of power exchange with the grid, their operation is different from grid-connected microgrids. Isolated microgrids, similar to grid-connected microgrids are equipped with energy management systems including unit commitment and economic dispatch modules. In this paper, the aim is to formulate the dynamic economic load dispatch (DELD) in isolated microgrids, while curtailment of responsive loads and curtailment of renewable power is allowed and load shedding is used as the last resort for balancing generation and demand. The generated power of dispatchable distributed generators (DGs), curtailed power of renewable DGs, curtailed demand and shed power are determined for each time period. The formulated DELD problem is solved with the well-established particle swarm optimisation (PSO) algorithm. The results for a microgrid with four dispatchable DGs and two renewable DGs show the performance of PSO over grey wolf optimisation (GWO) and also indicate the significant effect of demand response in reducing the operation cost of isolated microgrids.