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Abstract: Power system real time security assessment is one of the fundamental modules of the electricity markets. Typically, when a contingency occurs, it is required that security assessment and enhancement module shall be ready for action within about 20min time to meet the real time requirement. The recent California black out again highlighted the importance of system security. This paper proposed an approach for power system security assessment and enhancement based on the information provided from the pre-defined system parameter space. The proposed scheme opens up an efficient way for real time security assessment and enhancement in a competitive electricity market for single contingency case. [Copyright &y& Elsevier]

Highlights • Propose a joint planning model for active distribution systems. • Develop a new stochastic modelling approach for renewable energy. • Develop a new multi-objective optimization method for the proposed model. Abstract This paper proposes a multi-objective joint planning model for Active Distribution Networks (ADNs). The model determines the location and size of Electric Vehicle Charging Stations (EVCSs), Renewable Energy Sources (RESs), Battery Energy Storage System (BESSs), and distribution network expansion schemes, with the objectives of minimizing the total investment and reliability cost of the distribution network and maximizing the EVCSs' charging service capability. A scenario-based stochastic modelling approach based on Wasserstein distance metric and K-medoids scenario analysis is developed to model the stochastic nature of renewable generation. A multi-objective optimization algorithm, Multi-Objective Natural Aggregation Algorithm (MONAA), is applied to solve the proposed model. Case studies are conducted on a coupled 54-node distribution system and 25-node traffic system to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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]

The wake effect is the major obstacle to reaching the maximum power generation for wind farms, since choosing the suitable wake model that satisfies both computational cost and accuracy is a difficult task. Deep Reinforcement Learning (DRL) is a powerful data-driven method that can learn the optimal control policy without modeling the environment. However, the "trial and error" mechanism of DRL may cause high costs during the learning process. To address this issue, we propose an ensemble-based DRL wind farm control framework. Under this framework, a new algorithm called Actor Bagging Deep Deterministic Policy Gradient (AB-DDPG) is proposed, which combines the actor-network bagging method with the Deep Deterministic Policy Gradient. The gradient of the proposed method is proved to be consistent with the DDPG method. The experiment results in WFSim show that AB-DDPG can learn the optimal control policy with lower learning cost and a more robust learning process. • This paper proposes an ensemble-based DRL framework for wind farm control. • An algorithm is proposed under the proposed framework to reduce the learning cost. • The gradient used in the proposed algorithm is proved to be consistent with the DDPG. [ABSTRACT FROM AUTHOR]

• A new renewable energy trading system is proposed for residential communities. • The proposed energy trading system is highly scalable. • Homomorphic Encryption technology is introduced to protect user privacy. • Extensive numerical simulations are conducted to validate the proposed system. This paper proposes a new trading framework for enabling energy trading between end energy customers and a small-capacity Renewable Energy Plant (REP) in urban environments. To relieve the communication burden incurred by a large number of end customers who submit bids to purchase energy from the REP, the energy trading process is designed on a community basis. The end customers are grouped into multiple communities. With this approach, the proposed energy trading system coordinates the REP, the community energy management system in each community, and the local energy management system of each customer. The homomorphic encryption technology is integrated into the energy trading framework to enable the energy trading to be performed and settled without exposing individual customer's bidding energy purchase price and amount values. Extensive numerical simulations are conducted to validate the effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

• New security-constrained OPF model for a microgrid considering tie-line switching. • New Benders decomposition based solution algorithm to solve the OPF problem. • Stochastic optimization method with probabilistic modeling to address uncertainty. With the rapid development of microgrid, its tie-line switching from grid-connected to islanded mode is a topic worth discussing for considering both main grid resilience and microgrid security. In this paper, a stochastic security-constrained optimal power flow (OPF) method is proposed to deal with these conditions under high uncertainties. Firstly, a linear load flow model and a backward forward sweep algorithm are applied to present microgrid power flow with reduced computing burdens. Secondly, with consideration of tie-line switching from grid-connected to islanded operation mode, a security-constrained OPF problem for a microgrid is proposed to minimize operating cost and by optimizing microturbine setpoints and load shedding coefficient. To promise stable islanded operation after disconnection from the main grid, a Benders decomposition method is developed to decouple the OPF problem into a grid-connected master problem and an islanded sub-problem and then solve them iteratively with Benders cuts to guarantee microgrid security after tie-line switching. Last, a stochastic optimization method with probabilistic modelling is adopted to address the uncertainty issue caused by renewable energy sources and loads. The proposed stochastic security-constrained OPF method has been verified with high computing efficiency and robust security via comprehensive numerical simulations. [ABSTRACT FROM AUTHOR]

• A framework solution for multi-timescale coordinated operation of CHP and wind power. • Day-ahead and real-time uncertainties are handled by stochastic optimization. • The solution is applied to a real-life case in Denmark. • Comparative analysis between coordinated operation and independent operation. • Comparative analysis between stochastic and deterministic methods. This paper proposes a multi-timescale coordinated operation approach which coordinates the combined heat and power (CHP) plant and wind farm operation in a deregulated day-ahead heat market and a real-time balancing electricity market. This approach aims to optimize profits of the CHP-wind farm portfolio by considering energy sale in the day-ahead heat market and penalty in the real-time two-price balancing market. Multiple uncertainties of heat load, wind power generation, day-ahead electricity price, up- and down-regulation prices are taken into account for the optimal operation. A stochastic optimization method is applied to solve the proposed coordinated operation problem. The proposed multi-timescale coordinated operation approach is simulated on a real-life system in Copenhagen Denmark where an energy company owns both a CHP plant and a wind farm. The simulation results verify that the proposed method can achieve a profitable operation in the markets and a highly robust operation against the multiple uncertainties. [ABSTRACT FROM AUTHOR]

• Proposing an energy-based transient stability constrained unit commitment model. • Considering sensitivity of generation level and inertia on stability margin. • Proposing cutset energy function methodology without loss of network structure. • Developing a state transition MILP formulation for the unit commitment model. Rapid growth of load demand and concurrently system inertia deterioration put power systems at risk of transient instability. Therefore, operation of power systems as bulk complex systems must be secured against transient instability not only in hourly optimal power flow studies, but also in daily unit scheduling. To address this challenge, in this paper, secure and economic operation of power system is ensured through a Transient Stability-Constrained Unit Commitment (TSCUC) model employing Benders Decomposition (BD) technique. The proposed TSCUC model consists of one master problem determining committed units and two distinct sub-problems verifying the steady state impacts of single outages, and transient stability criteria, respectively. This paper proposes a state transition formulation for master problem as a mixed integer linear programming optimization problem preserving both compactness and tightness of the problem. A structure-preserving transient stability assessment approach called cutset energy function method is developed to assess the transient stability of the system for each configuration of the committed units, under a set of probable contingencies. Several case studies on a dynamic test system are demonstrated to validate the efficacy of the proposed TSCUC algorithm. Finally, the proposed method performance is compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• Small-signal stability analysis, frequency scan, time domain simulations are carried out to analyse the mechanism and characteristics of such SSI. • A real SSO incident is reported with its field measured data analysed. Replication of this incident in PSCAD/EMTDC is conducted to validate the consistency with theoretical analysis. • Composite short circuit ratio (CSCR) and relative short circuit ratio (RSCR) are developed to investigate characteristics of SSI with two different WPPs existing in the wind farm. Recently, sporadic power oscillations in subsynchronous frequency range were detected in several permanent magnet synchronous generator (PMSG)-based wind farms in Xinjiang, China. Given no existence of fixed series compensation in the system, it can be concluded that this is a new type of subsynchronous interaction (SSI) and different from the previous subsynchronous control interaction (SSCI) occurred in doubly-fed induction generator (DFIG)-based wind farms. Therefore, the characteristics and mechanism behind this phenomenon are so ambiguous that further clarification is needed. To investigate this new type of subsynchronous oscillations (SSO) problem, a real SSI incident is reported, and the equivalent model is established based on multi-machine equivalent aggregation. Refined frequency scan and small-signal analysis are carried out to analyse the mechanism and characteristics of such SSI. Time-domain simulations are conducted to replicate the incident. The results show that the interactions between PMSG-based wind farms and weak grids can trigger negative-resistance and capacitive-reactance effects, which leads to unstable power oscillations. To further investigate the influence of different wind power plants (WPPs), two short-circuit ratio metrics are developed, and time-domain simulations are conducted based on semi-aggregation. The potential hazards of this SSI and its mitigation strategies are also discussed and proposed. [ABSTRACT FROM AUTHOR]