An integrated optimization framework for combined heat and power units, distributed generation and plug-in electric vehicles

This paper proposes a calculation algorithm that creates operational points and evaluates the performance of distribution lines after reinforcement. The operational points of the line are probabilistically determined using Monte Carlo simulation for several objective functions for a given line. It is assumed that minimum voltage at all nodes has to be balanced to the maximum load served under variable distributed generation production, and to the energy produced from the intermittent renewables. The calculated maximum load, which is higher than the current load, is expected to cover the expected needs for electric vehicles charging. Following the proposed operational patterns, it is possible to have always maximum line capacity. This method is able to offer several benefits. It facilitates of network planning and the estimation of network robustness. It can be used as a tool for network planners, operators and large users. It applies to any type of network including radial and meshed.

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Creating markets for combined heat and power and clean distributed generation in New York State

Environmental Pollution ◽

10.1016/s0269-7491(03)00016-2 ◽

2003 ◽

Vol 123 (3) ◽

pp. 451-462 ◽

Cited By ~ 19

Author(s):

Thomas G Bourgeois ◽

Bruce Hedman ◽

Fred Zalcman

Keyword(s):

New York ◽

Distributed Generation ◽

New York State ◽

Combined Heat And Power ◽

York State

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Development of a Kernel Extreme Learning Machine Model for Capacity Selection of Distributed Generation Considering the Characteristics of Electric Vehicles

Applied Sciences ◽

10.3390/app9122401 ◽

2019 ◽

Vol 9 (12) ◽

pp. 2401

Author(s):

Zhongdong Yin ◽

Jingjing Tu ◽

Yonghai Xu

Keyword(s):

Extreme Learning Machine ◽

Distributed Generation ◽

Electric Vehicles ◽

Distribution Network ◽

Support Vector ◽

Kernel Extreme Learning Machine ◽

Learning Machine ◽

The Stability ◽

Capacity Selection ◽

Selection Of

The large-scale access of distributed generation (DG) and the continuous increase in the demand of electric vehicle (EV) charging will result in fundamental changes in the planning and operating characteristics of the distribution network. Therefore, studying the capacity selection of the distributed generation, such as wind and photovoltaic (PV), and considering the charging characteristic of electric vehicles, is of great significance to the stability and economic operation of the distribution network. By using the network node voltage, the distributed generation output and the electric vehicles’ charging power as training data, we propose a capacity selection model based on the kernel extreme learning machine (KELM). The model accuracy is evaluated by using the root mean square error (RMSE). The stability of the network is evaluated by voltage stability evaluation index (Ivse). The IEEE33 node distributed system is used as simulation example, and gives results calculated by the kernel extreme learning machine that satisfy the minimum network loss and total investment cost. Finally, the results are compared with support vector machine (SVM), particle swarm optimization algorithm (PSO) and genetic algorithm (GA), to verify the feasibility and effectiveness of the proposed model and method.

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Optimal Control of Multiple Microgrids and Buildings by an Aggregator

Energies ◽

10.3390/en13051058 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1058 ◽

Cited By ~ 3

Author(s):

Giulio Ferro ◽

Riccardo Minciardi ◽

Luca Parodi ◽

Michela Robba ◽

Mansueto Rossi

Keyword(s):

Distributed Generation ◽

Electric Vehicles ◽

Demand Response ◽

Electrical Grid ◽

Energy Management Systems ◽

New Energy ◽

Systems Models ◽

Fair Assignment ◽

Demand Response Programs

The electrical grid has been changing in the last decade due to the presence of renewables, distributed generation, storage systems, microgrids, and electric vehicles. The introduction of new legislation and actors in the smart grid’s system opens new challenges for the activities of companies, and for the development of new energy management systems, models, and methods. A new optimization-based bi-level architecture is proposed for an aggregator of consumers in the balancing market, in which incentives for local users (i.e., microgrids, buildings) are considered, as well as flexibility and a fair assignment in reducing the overall load. At the lower level, consumers try to follow the aggregator’s reference values and perform demand response programs to contain their costs and satisfy demands. The approach is applied to a real case study.

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