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

The smart grid is widely considered to be the informationization of the power grid. As an essential characteristic of the smart grid, demand response can reschedule the users’ energy consumption to reduce the operating expense from expensive generators, and further to defer the capacity addition in the long run. This survey comprehensively explores four major aspects: 1) programs; 2) issues; 3) approaches; and 4) future extensions of demand response. Specifically, we first introduce the means/tariffs that the power utility takes to incentivize users to reschedule their energy usage patterns. Then we survey the existing mathematical models and problems in the previous and current literatures, followed by the state-of-the-art approaches and solutions to address these issues. Finally, based on the above overview, we also outline the potential challenges and future research directions in the context of demand response.

A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches

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

Microgrid is a key enabling solution to future smart grids by integrating distributed renewable generators and storage systems to efficiently serve the local demand. However, due to the random and intermittent characteristics of renewable energy, new challenges arise for the reliable operation of microgrids. To address this issue, we study in this paper the real-time energy management for a single microgrid system that constitutes a renewable generation system, an energy storage system, and an aggregated load. We model the renewable energy offset by the load over time, termed net energy profile, to be practically predictable, but with finite errors that can be arbitrarily distributed. We aim to minimize the total energy cost (modeled as sum of time-varying strictly convex functions) of the conventional energy drawn from the main grid over a finite horizon by jointly optimizing the energy charged/discharged to/from the storage system over time subject to practical load and storage constraints. To solve this problem in real time, we propose a new off-line optimization approach to devise the online algorithm. In this approach, we first assume that the net energy profile is perfectly predicted or known ahead of time, under which we derive the optimal off-line energy scheduling solution in closed-form. Next, inspired by the optimal off-line solution, we propose a sliding-window based online algorithm for real-time energy management under the practical setup of noisy predicted net energy profile with arbitrary errors. Finally, we conduct simulations based on the real wind generation data of the Ireland power system to evaluate the performance of our proposed algorithm, as compared with other heuristically designed algorithms, as well as the conventional dynamic programming based solution.

Real-Time Energy Storage Management for Renewable Integration in Microgrid: An Off-Line Optimization Approach

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

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

Considerable research has been devoted on the development of decision-making models suitable for tackling decision problems integrating some amount of uncertainty However, such approaches are either problem-specific, either too general to suit certain problems In this paper we propose an approach for performing decision-making under uncertainty suitable for problems in which decisions must be made sequentially (ie: in time) We apply the proposed model to the case of a virtual power plant (comprising a wind farm and an energy storage device) operating under market conditions with the objective of reducing the imbalances generated by the plant while maximizing the profit of the plant operator Using real-world data, we have simulated and evaluated several operation scenarios for an entire year of operation Wind power forecasts produced by a state-of-art wind power forecasting model (based on KDE estimators) were used

https://ieeexplore.ieee.org/iel5/4912595/4912596/04912609.pdf

A Spot-Risk-Based Approach for Addressing Problems of Decision-Making under Uncertainty

Independent power producers have the possibility to participate in short-term electricity markets to trade wind power energy in several countries in Europe. Under such market context, penalties may apply for differences between the contracted energy and the produced energy. The limited predictability of the wind resource may thus result to a reduction of the competitiveness of wind power generation. In this paper, we propose a risk-based decision approach for optimizing the benefits of an energy producer who submits energy bids in a day-ahead electricity market. Loss functions are used to model the penalties resulting from imbalances. For achieving this, we use wind power probabilistic forecasts. The benefits from the approach are demonstrated using real-word data for a whole year.

https://hal-mines-paristech.archives-ouvertes.fr/hal-00506067/document

Advanced strategies for wind power trading in short-term electricity markets

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