Energy Infrastructure of the Factory as a Virtual Power Plant: Smart Energy Management

The advent of distributed energy resources (DERs), such as distributed renewables, energy storage, electric vehicles, and controllable loads, brings a significantly disruptive and transformational impact on the centralized power system. It is widely accepted that a paradigm shift to a decentralized power system with bidirectional power flow is necessary to the integration of DERs. The virtual power plant (VPP) emerges as a promising paradigm for managing DERs to participate in the power system. In this paper, we develop a blockchain-based VPP energy management platform to facilitate a rich set of transactive energy activities among residential users with renewables, energy storage, and flexible loads in a VPP. Specifically, users can interact with each other to trade energy for mutual benefits and provide network services, such as feed-in energy, reserve, and demand response, through the VPP. To respect the users’ independence and preserve their privacy, we design a decentralized optimization algorithm to optimize the users’ energy scheduling, energy trading, and network services. Then we develop a prototype blockchain network for VPP energy management and implement the proposed algorithm on the blockchain network. By experiments using real-world data trace, we validated the feasibility and e_ectiveness of our algorithm and the blockchain system. The simulation results demonstrate that our blockchain-based VPP energy management platform reduces the users’ cost by up to 38.6% and reduces the overall system cost by 11.2%.

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Water-Energy Management for Demand Charges and Energy Cost Optimization of a Pumping Stations System under a Renewable Virtual Power Plant Model

Energies ◽

10.3390/en13112900 ◽

2020 ◽

Vol 13 (11) ◽

pp. 2900

Author(s):

Natalia Naval ◽

Jose M. Yusta

Keyword(s):

Power Plant ◽

Energy Management ◽

Energy Cost ◽

Irrigation System ◽

Market Price ◽

Optimal Operation ◽

Mixed Integer ◽

Virtual Power ◽

Virtual Power Plant ◽

Operating Profit

The effects of climate change seriously affect agriculture at different latitudes of the planet because periods of drought are intensifying and the availability of water for agricultural irrigation is reducing. In addition, the energy cost associated with pumping water has increased notably in recent years due to, among other reasons, the maximum demand charges that are applied annually according to the contracted demand in each facility. Therefore, very efficient management of both water resources and energy resources is required. This article proposes the integration of water-energy management in a virtual power plant (VPP) model for the optimization of energy costs and maximum demand charges. For the development of the model, a problem related to the optimal operation of electricity generation and demand resources arises, which is formulated as a nonlinear mixed-integer programming model (MINLP). The objective is to maximize the annual operating profit of the VPP. It is worth mentioning that the model is applied to a large irrigation system using real data on consumption and power generation, exclusively renewable. In addition, different scenarios are analyzed to evaluate the variability of the operating profit of the VPP with and without intraday demand management as well as the influence of the wholesale electricity market price on the model. In view of the results obtained, the model that integrates the management of the water-energy binomial increases the self-consumption of renewable energy and saves electricity supply costs.

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A load-balancing power scheduling system for virtual power plant considering emission reduction and charging demand of moving electric vehicles

Measurement and Control ◽

10.1177/0020294019836114 ◽

2019 ◽

Vol 52 (5-6) ◽

pp. 687-701

Author(s):

Chenn-Jung Huang ◽

An-Feng Liu ◽

Kai-Wen Hu ◽

Liang-Chun Chen ◽

Yu-Kang Huang

Keyword(s):

Power Generation ◽

Power Plant ◽

Energy Management ◽

Energy Sources ◽

Virtual Power ◽

Short Term ◽

Scheduling System ◽

Virtual Power Plant ◽

Power Scheduling ◽

Term Energy

With the rapid development of the emerging technologies and significant cost reduction of the deployment for solar energy and wind power, the replacement of traditional power generation by renewable energy becomes feasible in the future. However, different from currently deployed centralized power sources, renewables are categorized as one kind of intermittent energy sources, and the scale of renewables is small and scattered. In the recent literature, the architecture of virtual power plant was proposed to replace the current smart grid in the future. However, the energy sharing concept and the uncertainties of intermittent energy sources will cause the short-term energy management for the virtual power plant much more complicated than the current centralized control energy management for traditional power generation system. We thus propose a hierarchical day-ahead power scheduling system for virtual power plant in this work to tackle the complex short-term energy management problems. We first collect electricity consumption data from smart appliances used in households and predict power-generating capacity of renewable energy sources at the prosumer level. Then, the proposed hierarchical power scheduling system is employed to schedule the usage of electricity for the customers by considering the efficiency of the use of distributed renewables. Notably, charging management of a moving electric vehicle is also considered in the proposed power scheduling mechanism. In addition, a real-time power tracking mechanism is presented to deal with the forecast errors of volatile renewable power generation, electricity load, and moving electric vehicle charging, and the maximal usage of renewables and reduction of the burden on community virtual power plants during time period of peak load can be achieved accordingly. The experimental results show that the proposed day-ahead power scheduling system can mitigate the dependency on traditional power generation effectively, and balance peak and off-peak period load of electricity market.

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