We propose an Economic Probabilistic analogy: the category of cost is analogous to the category of Probability. The proposed analogy permits construction of an informal theory of nonlinear non-convex Gaussian Utility and Cost, which describes the real economic processes more adequately than a theory based on a linear and convex models. Based on the proposed analogy, we build a nonlinear non-con...

The coming interaction between a growing electrified vehicle fleet and the desired growth in renewable energy provides new insights into the economic dispatch (ED) problem. This paper presents an economic dispatch model that considers electric vehicle charging, battery exchange stations, and wind farms. This ED model is a high-dimensional, non-linear, and stochastic problem and its solution req...

At the central energy management center in a power system, the real time controls continuously track the load changes and endeavor to match the total power demand with total generation in such a manner that the operating cost is minimized while all the operating constraints are satisfied. However, due to the strict government regulations on environmental protection, operation at minimum cost is...

In order to cope with the efficient consumption and flexible regulation of resource scarcity due grid integration renewable energy sources, a scheduling strategy that takes into account coordinated interaction source, grid, load, storage is proposed. improve accuracy dispatch, BP neural network approach modified by genetic algorithm used predict sources loads. The non-convex, non-linear optimal...

This two-part paper develops a non-iterative coordinated optimal dispatch framework, i.e., free of iterative information exchange, via the innovation equivalent projection (EP) theory. The EP eliminates internal variables from technical and economic operation constraints subsystem obtains an model with reduced scale, which is key to optimization. In Part II this paper, novel algorithm explicit ...

In recent years increasing of thermal power plants air pollution and concentration of carbon dioxide emission leads to the global warming. This paper solves the economic dispatch problem includes the dispatch of systems to the minimize carbon dioxide emissions, as well as to achieve the minimum fuel cost. This paper proposed a lambda based approach for solving the Combined Economic and Emission...

The Cascaded Short-Term Hydrothermal Scheduling (CSTHTS) problem is a highly non-linear, multi-modal, non-convex, and NP-hard optimization that has been solved by conventional metaheuristic algorithms in the past. As CSTHTS falls under category of applied operational research, therefore, work still on-going to find new variants existing would better approximate optimal global solution shorter c...

Abstract: In this paper a solution to an highly constrained and non-convex economical dispatch (ED) problem with a meta-heuristic technique named Sensing Cloud Optimization (SCO) is presented. The proposed meta-heuristic is based on a cloud of particles whose central point represents the objective function value and the remaining particles act as sensors "to fill" the search space and "guide" t...

Abstract Climate change and environmental protection have a significant impact on thermal plants. So, the main principles of combined economic-emission dispatch (CEED) problem are indeed to reduce greenhouse gas emissions fuel costs. Many approaches demonstrated their efficacy in addressing CEED problem. However, designing robust algorithm capable achieving Pareto optimal solutions under its mu...

Urban rail transit brings huge economic and social benefits to the society, however, the event of accidents and breakdown of subway can cause a wide range of traffic impact, significant personal and economic losses and even cause traffic paralysis. Current domestic emergency dispatch and command model is basically "man-based", therefore the validity and effectiveness of emergency scheduling dec...