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The increasing integration of distributed renewable energy resources highlights the need to design new control strategies for hybrid wind turbine-photovoltaic (WT-PV)-battery microgrid (MG) clusters. This paper proposes a two-level optimization model for the coordinated energy management between distribution systems and clustered WT-PV-battery MGs. The upper level of the model deals with the operation of the distribution network, while the lower level considers the coordinated operation of multiple MGs. An interactive game matrix (IGM) is applied to coordinate the power exchange among multiple MGs, and between the distribution network and MGs. The model is solved by a modified hierarchical genetic algorithm. Case studies on a distribution system with MGs, as well as a practical multi-MG system, demonstrate the effectiveness of the proposed method in improving power quality, reliability, and environmental benefits. [ABSTRACT FROM AUTHOR]

This paper proposes a necessary condition for power flow insolvability in power distribution systems with distributed generators (DGs). We show that the proposed necessary condition indicates the impending singularity of the Jacobian matrix and the onset of voltage instability. We consider different operation modes of DG inverters, e.g., constant-power and constant-current operations, in the proposed method. A new index based on the presented necessary condition is developed to indicate the distance between the current operating point and the power flow solvability boundary. Compared to existing methods, the operating condition-dependent critical loading factor provided by the proposed condition is less conservative and is closer to the actual power flow solution space boundary. The proposed method only requires the present snapshots of voltage phasors to monitor the power flow insolvability and voltage stability. Hence, it is computationally efficient and suitable to be applied to a power distribution system with volatile DG outputs. The accuracy of the proposed necessary condition and the index is validated by simulations on a distribution test system with different DG penetration levels. [ABSTRACT FROM AUTHOR]

This paper proposes a novel comprehensive operation and self-healing strategy for a distribution system with both dispatchable and nondispatchable distributed generators (DGs). In the normal operation mode, the control objective of the system is to minimize the operation costs and maximize the revenues. A rolling-horizon optimization method is used to schedule the outputs of dispatchable DGs based on forecasts. In the self-healing mode, the on-outage portion of the distribution system will be optimally sectionalized into networked self-supplied microgrids (MGs) so as to provide reliable power supply to the maximum loads continuously. The outputs of the dispatchable DGs will be rescheduled accordingly too. In order to take into account the uncertainties of DG outputs and load consumptions, we formulate the problems as a stochastic program. A scenario reduction method is applied to achieve a tradeoff between the accuracy of the solution and the computational burden. A modified IEEE 123-node distribution system is used as a test system. The results of case studies demonstrate the effectiveness of the proposed methodology. [ABSTRACT FROM PUBLISHER]

Due to the long planning horizon, transmission expansion planning is typically subjected to a lot of uncertainties including load growth, renewable energy penetration, policy changes, etc. In addition, deregulation of the power industry and pressure from climate change introduced new sources of uncertainties on the generation side of the system. Generation expansion and retirement become highly uncertain as well. Some of the uncertainties do not have probability distributions, making it difficult to use stochastic programming. Techniques like robust optimization that do not require a probability distribution became desirable. To address these challenges, we study two optimization criteria for the transmission expansion planning problem under the robust optimization paradigm, where the maximum cost and maximum regret of the expansion plan over all uncertainties are minimized, respectively. With these models, our objective is to make planning decisions that are robust against all scenarios. We use a two-layer algorithm to solve the resulting tri-level optimization problems. Then, in our case studies, we compare the performance of the minimax cost approach and the minimax regret approach under different characterizations of uncertainties. [ABSTRACT FROM AUTHOR]

Conservation voltage reduction (CVR) is widely adopted by utilities for peak demand reduction and energy savings through reducing the voltage level of the electrical distribution system. This paper presents an in-depth review on implementing and assessing CVR. The methodologies to quantify CVR effects are categorized into comparison-based, regression-based, synthesis-based and simulation-based methods. The implementation strategies for voltage reduction are classified into open-loop and closed-loop methods. The impacts of emerging smart-grid technologies on CVR are also discussed. The paper can provide researchers and utility engineers with further insights into the state of the art, technical barriers and future research directions of CVR technologies. [ABSTRACT FROM PUBLISHER]

To accommodate the large scale of renewable energy resources now widely integrated into power systems, an interactive two-level pricing mechanism for coupled microgrids (MG) in a smart energy system is proposed that considers operational quality and renewable generation uncertainty. In the upper level of the pricing mechanism, the distribution energy market operator (DEMO) guarantees operational quality by trading energy with coupled microgrids, while the actual transactions between networked microgrids is performed at the lower level. Stochastic programming is applied to handle the uncertainty caused by large-scale renewable integration. An innovative time-varying game vector and energy transaction strategy deal with the spatio-temporal market interaction of the networked microgrids, where each microgrid is able to directly trade all types of energy with any other microgrid at any time. The proposed model is solved using a customized hierarchical genetic algorithm. Case studies on an IEEE bus test feeder and an existing energy system in China demonstrate the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]