A review of microgrid development in the United States – A decade of progress on policies, demonstrations, controls, and software tools

Microgrids have become increasingly popular in the United States. Supported by favorable federal and local policies, microgrid projects can provide greater energy stability and resilience within a project site or community. This paper reviews major federal, state, and utility-level policies driving microgrid development in the United States. Representative U.S. demonstration projects are selected and their technical characteristics and non-technical features are introduced. The paper discusses trends in the technology development of microgrid systems as well as microgrid control methods and interactions within the electricity market. Software tools for microgrid design, planning, and performance analysis are illustrated with each tool's core capability. Finally, the paper summarizes the successes and lessons learned during the recent expansion of the U.S. microgrid industry that may serve as a reference for other countries developing their own microgrid industries

A Black-Box Discrete Optimization Benchmarking (BB-DOB) Pipeline Survey: Taxonomy, Evaluation, and Ranking

This paper provides a taxonomical identification survey of classes in discrete optimization challenges that can be found in the literature including a proposed pipeline for benchmarking, inspired by previous computational optimization competitions. Thereby, a Black-Box Discrete Optimization Benchmarking (BB-DOB) perspective is presented for the BB-DOB@GECCO Workshop. It is motivated why certain classes together with their properties (like deception and separability or toy problem label) should be included in the perspective. Moreover, guidelines on how to select significant instances within these classes, the design of experiments setup, performance measures, and presentation methods and formats are discussed.authorsversio

Gravitational Swarm Optimizer for Global Optimization

In this article, a new meta-heuristic method is proposed by combining particle swarm optimization (PSO) and gravitational search in a coherent way. The advantage of swarm intelligence and the idea of a force of attraction between two particles are employed collectively to propose an improved meta-heuristic method for constrained optimization problems. Excellent constraint handling is always required for the success of any constrained optimizer. In view of this, an improved constraint-handling method is proposed which was designed in alignment with the constitutional mechanism of the proposed algorithm. The design of the algorithm is analyzed in many ways and the theoretical convergence of the algorithm is also established in the article. The e�fficiency of the proposed technique was assessed by solving a set of 24 constrained problems and 15 unconstrained problems which have been proposed in IEEE-CEC sessions 2006 and 2015, respectively. The results are compared with 11 state-of-the-art algorithms for constrained problems and 6 state-of-the-art algorithms for unconstrained problems. A variety of ways are considered to examine the ability of the proposed algorithm in terms of its converging ability, success, and statistical behavior. The performance of the proposed constraint-handling method is judged by analyzing its ability to produce a feasible population. It was concluded that the proposed algorithm performs e�fficiently with good results as a constrained optimizer

Optimal management of reactive power sources in far-offshore wind power plants

This paper introduces a new approach for the optimal management of reactive power sources, which follows a predictive optimization scheme (i.e. day-ahead, intraday application). Predictive optimization is based to the principle of minimizing the real power losses, as well the number of On-load Tap Changer (OLTC) operations for 24 time steps ahead. The mixed-integer nature of the problem and the restricted computing budget is tackled by using an emerging metaheuristic algorithm called Mean-Variance Mapping Optimization (MVMO). The evolutionary mechanism of MVMO is enhanced by introducing a new mapping function, which improves its global search capability. The effectiveness of MVMO (i.e. fast convergence and robustness against randomness in initialization and factors used in evolutionary operations) and the achievement of optimal grid code compliance are demonstrated by investigating the case of a far-offshore wind power plant, interconnected with HVDC link

Time trends in radiocaesium in the Japanese diet following nuclear weapons testing and Chernobyl:implications for long term contamination post-Fukushima

Estimation of time changes in radiocaesium in foodstuffs is key to predicting the long term impact of the Fukushima accident on the Japanese diet. We have modelled >4000 measurements, spanning 50 years, of 137Cs in foodstuffs and whole diet in Japan after nuclear weapons testing (NWT) and the Chernobyl accident. Broadly consistent long term trends in 137Cs activity concentrations are seen between different agricultural foodstuffs; whole diet follows this general trend with remarkably little variation between averages for different regions of Japan. Model blind tests against post-NWT data for the Fukushima Prefecture showed good predictions for radiocaesium in whole diet, spinach and Japanese radish (for which good long term test data were available). For the post-Fukushima period to 2015, radiocaesium in the average diet followed a declining time trend consistent with that seen after NWT and Chernobyl. Data for different regions post-Fukushima show a high degree of mixing of dietary foodstuffs between regions: predictions which assumed that only regionally produced food was consumed significantly over-estimated empirical data. Predictions of average committed effective internal doses from dietary 137Cs (2011 to 2061) in nonevacuated parts of the Fukushima Prefecture show that average internal dose is relatively low. This study focuses on average regional ingestion dose rates and does not attempt to make site specific predictions. However, the temporal trends identified could form a basis for site specific predictions of long term activity concentrations in agricultural products and diet both outside and (to assess potential re-use) inside currently evacuated areas

Multi - island competitive cooperative coevolution for real parameter global optimization

Problem decomposition is an important attribute of cooperative coevolution that depends on the nature of the problems in terms of separability which is defined by the level of interaction amongst decision variables. Recent work in cooperative coevolution featured competition and collaboration of problem decomposition methods that was implemented as islands in a method known as competitive island cooperative coevolution (CICC). In this paper, a multi-island competitive cooperative coevolution algorithm (MICCC) is proposed in which several different problem decomposition strategies are given a chance to compete, collaborate and motivate other islands while converging to a common solution. The performance of MICCC is evaluated on eight different benchmark functions and are compared with CICC where only two islands were utilized. The results from the experimental analysis show that competition and collaboration of several different island can yield solutions with a quality better than the two-island competition algorithm (CICC) on most complex multi-modal problems