There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

In this Chapter, we imagine a decision maker (or a group of experts) trying to establish or examine fair procedures to combine opinions about alternatives related to different points of view.

Multiple criteria decision making

Emerging new technologies like distributed generation, distributed storage, and demand-side load management will change the way we consume and produce energy. These techniques enable the possibility to reduce the greenhouse effect and improve grid stability by optimizing energy streams. By smartly applying future energy production, consumption, and storage techniques, a more energy-efficient electricity supply chain can be achieved. In this paper a three-step control methodology is proposed to manage the cooperation between these technologies, focused on domestic energy streams. In this approach, (global) objectives like peak shaving or forming a virtual power plant can be achieved without harming the comfort of residents. As shown in this work, using good predictions, in advance planning and real-time control of domestic appliances, a better matching of demand and supply can be achieved.

/pdf/management-and-control-of-domestic-smart-grid-technology-4vnouaa4mi.pdf

Management and Control of Domestic Smart Grid Technology

Offering model for a virtual power plant based on stochastic programming

Wind power is gaining in significance as an important renewable source of clean energy. However, due to their inherent uncertainty, wind generators are often unable to participate in the forward electricity markets like the more predictable and controllable conventional generators. Given this, virtual power plants (VPPs) are being advocated as a solution for increasing the reliability of such intermittent renewable sources. In this paper, we take this idea further by considering VPPs as coalitions of wind generators and electric vehicles, where wind generators seek to use electric vehicles (EVs) as a storage medium to overcome the vagaries of generation. Using electric vehicles in this manner has the advantage that, since the number of EVs is increasing rapidly, no initial investment in dedicated storage is needed. In more detail, we first formally model the VPP and then, through an operational model based on linear programming, we show how the supply to the Grid and storage in the EV batteries can be scheduled to increase the profit of the VPP, while also paying for the storage using a novel scheme. The feasibility of our approach is examined through a realistic case-study, using real wind power generation data, corresponding electricity market prices and electric vehicles' characteristics.

/pdf/an-agent-based-approach-to-virtual-power-plants-of-wind-377dfc9kg8.pdf

An Agent-Based Approach to Virtual Power Plants of Wind Power Generators and Electric Vehicles

Under electricity market operation, the competitiveness of wind power generation may be reduced because of the stochastic nature of the wind resource, which often results in increased regulation costs. The negative impact associated to the stochastic nature of the wind resource may be reduced by coupling the wind farm with energy storage facilities, thus constituting a virtual power plant. In this paper, a novel method is proposed for scheduling and operating such a plant in an electricity market environment. The proposed method is able to take advantage of existing market opportunities for increasing operational profits while smoothing out energy imbalances caused by the errors associated to wind power forecasts. The results obtained for a test case based on real-world data for a whole year of operation are compared and discussed.

Management of Energy Storage Coordinated with Wind Power under Electricity Market Conditions

When participating in an electricity market, wind power generation may be penalized by increased regulation costs due the stochastic nature of the wind resource. The negative impact associated to the stochastic nature of wind may be reduced by coupling the wind farm with energy storage facilities, thus constituting a virtual power plant. In this paper, focus is put on advanced methods for reducing regulation costs. A novel method is proposed for the intra-day scheduling and operation of such a plant in an electricity market environment. Such method is able to minimize the imbalance penalty risks associated to wind power forecast uncertainty through a rolling-window approach. Results based on a real-world test case are presented and discussed.

/pdf/risk-based-strategies-for-wind-pumped-hydro-coordination-30xrzw4wg6.pdf

Risk-based strategies for wind/pumped-hydro coordination under electricity markets

In several countries, independent wind power producers have the possibility to participate in short-term electricity markets for trading their production. However, the limited predictability of the wind resource may lead to differences between produced and contracted energy, thus generating energy imbalances. This may result in the payment of imbalance penalties, which leads to a reduction of the competitiveness of wind power generation. This paper develops a methodology, suitable for independent wind power producers, that permits them to participate in an efficient way simultaneously in several sequential electricity markets, namely day-ahead and intraday markets. The considered intraday market takes place through a continuous trading mechanism. The imbalance cost reduction related to the adjustment participation in the intraday market is assessed using a real-world test case.

https://hal-mines-paristech.archives-ouvertes.fr/hal-00505144/document

Strategies for Wind Power Trading in Sequential Short-Term Electricity Markets

In the context of the EU FP7 AMBASSADOR project, a simulation platform has been developed, as a support for the development and the deployment of energy management systems at the district level. Such simulation platform is called District Simulation Platform (DSP), and includes both the models of the physical components from the district, and the models of the energy management algorithms. The DSP is a support for Software In the Loop (SIL) validation: the same algorithm code is first developed and tested on the DSP before being deployed for real-time operation of the district. The DSP can take into account various district configurations through user-defined configuration files. These configurations cover the field tests of the AMBASSADOR project, including the Lavrion experimental site from NTUA close to Athens (Greece), and the INCAS experimental district platform from CEA close to Chambery (France). The last one is detailed in this paper.

Simulation for the evaluation of energy management algorithms at the district level - Example of use case from the AMBASSADOR project

This paper proposes a new method, based on stochastic optimization, for managing the operation of a wind farm in conjunction with energy storage. The main objective is to maximize the benefit of the joint operation of wind generators and energy storage devices, while considering the stochastic nature of wind power production and electricity market prices. The paper presents results of the application of the methodology in decision-making processes related to wind power management.

https://hal-mines-paristech.archives-ouvertes.fr/hal-00506077/document

Stochastic optimization techniques for the optimal combination of wind power generation and energy storage in a market environment

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