Cloud-based energy management service: Design, analysis, and realization

With the aroused attentions on promoting renewable energy and the increasing penetration of distributed energy resources (DER) and the electric vehicles (EVs), providing the energy management tools efficiently for operating DERs and EVs grid-friendly and attracting customers to involve the management have become the important issues. An extensive cloud-based framework is firstly proposed to provide the energy management as a service (EMaaS) for customers (i.e., DERs owners). Customers who are involved in the same EMaaS form the ``community to trade their produced renewable energy virtually among others. By facilitating the DERs, storage systems, and the customers\u27 trading choices within the same community, incentives are maximized as the global cost is minimized and renewable energy integration is enhanced as the renewable energy consumption is stabilized by the proposed EMaaS for each community. To further attract customers not only involve in controlling their consumption patterns but also participate actively, and operate EVs and DERs within the community grid-friendly, the fair demand response with the EV is secondly realized for the cloud-based energy management service (F-DREV). The choices of electricity usage and trading are combined to further minimize the global cost for each community while distributing incentives fairly to the individual customer. The cross-community interaction (XCI) and adjustment (XCI) are thirdly proposed for the cloud-based energy management. XCI minimizes the global costs for the collaborated communities and is performed in the distributed fashion to overcome the privacy concern and the difficulty for handling the large-scale data. XCA enhances the efficiency of XCI under uncertainty, where the overwhelmed data exchanging and the computations can be significantly reduced

Intelligent Decision Support System for Energy Management in Demand Response Programs and Residential and Industrial Sectors of the Smart Grid

This PhD thesis addresses the complexity of the energy efficiency control problem in residential and industrial customers of Smart electrical Grid, and examines the main factors that affect energy demand, and proposes an intelligent decision support system for applications of demand response. A multi criteria decision making algorithm is combined with a combinatorial optimization technique to assist energy managers to decide whether to participate in demand response programs or obtain energy from distributed energy resources

Intermediation in Future Energy Markets: Innovative Product Design and Pricing

In order to mitigate the impacts of climate change, the international community envisages significant investments in electricity generation from renewable energy sources (RES). The integration of this decentralized and fluctuating type electricity generation poses several challenges to planning, operation, and economics of power systems. The established energy systems were originally designed for a centralized electricity generation that follows the uncontrolled but well predictable demand. However, for large shares of RES, relying only on the flexibility of the generation side would be economically inefficient. Furthermore, the environmental benefits of using RES would be depleted by additional carbon emissions from ramping highly flexible fossil-fueled power plants. An appealing alternative to facilitate the efficient integration of large shares of RES is to exploit the so far mainly passive demand side as an additional source of flexibility. The established centralized approaches can hardly handle the fine-grained and decentralized nature of demand side flexibility. Therefore, the intermediation between centralized control and decentralized demand will play a major role in future energy markets, which constitutes the overarching topic of this dissertation. Typically electricity generation from RES is capital-intensive but has near zero marginal costs. On this account, novel services need to be offered in order to transmit the right economic signals. To this end, the concept of the differentiable good electricity is refined in this dissertation. Embedded into the so-called energy service, characteristics such as temporal and spatial price differentiation or the risk of interruption can be specified to differentiate the so far homogeneous good. Based on the morphological design theory a framework for the notion of energy services is established and subsequently implemented as a decision support system. This supports a systematic and structured product development process to design innovative energy services. Such an innovative energy service is, e.g., the charging of electric vehicles in car parks, where prices are differentiated by job completion deadline. This allows the car park operator to control the aggregated load of all charging jobs to follow local RES generation. Based on this energy service the downstream activity of an intermediary is formally modeled as an optimization problem and evaluated by means of an empirical simulation experiment. The results provide insights on pricing policy and the value of demand side flexibility with regard to both the integration of local RES generation and operative profit optimization. In order to illustrate another innovative energy service the presented model is extended by the upstream activity of the intermediary. Household consumers are offered monetary incentives if they allow the intermediary to control their appliances. The results indicate the cost saving potential from demand side flexibility for the intermediary\u27s procurement of electricity. Beyond that, this model formulation constitutes the foundation for further examinations, e.g., to study the strategic behavior of intermediaries on real-time electricity markets that are prone to market power abuse due to low market liquidity

New market designs in electricity market simulation models: Deliverable D4.5

Project TradeRES - New Markets Design & Models for 100% Renewable Power Systems: https://traderes.eu/about/ABSTRACT: To integrate a high share of renewables in a future system, several modifications to the electricity market rules may need to be implemented. The most relevant market design concepts were identified from the literature and reported in work package 3. There are several uncertainties, for instance with respect to the questions of whether a future electricity market will provide enough incentives for investment in variable renewable energy sources (vRES) – mainly solar and wind energy – and in flexibility options, especially for long periods with insufficient vRES generation. In this deliverable, the modelling requirements to analyse the new market rules are determined. The modelling efforts will reflect the main policy choices and are based on the strengths of the modelling capabilities from the consortium. The model enhancements to represent the temporal, spatial and sectoral flexibility will be approached in deliverables 4.1 to 4.3. For this reason, these topics will be described only briefly in this deliverable.N/

Optimization of Electric-Vehicle Charging: scheduling and planning problems

The progressive shift from traditional vehicles to Electric Vehicles (EVs ) is considered one of the key measures to achieve the objective of a significant reduction in the emission of pollutants, especially in urban areas. EVs will be widely used in a not-so-futuristic vision, and new technologies will be present for charging stations, batteries, and vehicles. The number of EVs and Charging Stations (CSs) is increased in the last years, but, unfortunately, wide usage of EVs may cause technical problems to the electrical grid (i.e., instability due to intermittent distributed loads), inefficiencies in the charging process (i.e., lower power capacity and longer recharging times), long queues and bad use of CSs. Moreover, it is necessary to plan the CSs installation over the territory, the schedule of vehicles, and the optimal use of CSs. This thesis focuses on applying optimization methods and approaches to energy systems in which EVs are present, with specific reference to planning and scheduling decision problems. In particular, in smart grids, energy production, and storage systems are usually scheduled by an Energy Management System (EMS) to minimize costs, power losses, and CO2 emissions while satisfying energy demands. When CSs are connected to a smart grid, EVs served by CSs represent an additional load to the power system to be satisfied, and an additional storage system in the case of vehicle-to-grid (V2G) technology is enabled. However, the load generated by EVs is deferrable. It can be thought of as a process in which machines (CSs) serve customers/products (EVs) based on release time, due date, deadline, and energy request, as happens in manufacturing systems. In this thesis, first, attention is focused on defining a discrete-time optimization problem in which fossil fuel production plants, storage systems, and renewables are considered to satisfy the grid's electrical load. The discrete-time formalization can use forecasting for renewables and loads without data elaboration. On the other side, many decision variables are present, making the optimization problem hard to solve through commercial optimization tools. For this reason, an alternative method for the optimal schedule of EVs characterized by a discrete event formalization is presented. This new approach can diminish the number of variables by considering the time intervals as variables themselves. Of course, the solution's optimality is not guaranteed since some assumptions are necessary. Moreover, the last chapter proposes a novel approach for the optimal location and line assignment for electric bus charging stations. In particular, the model provides the siting and sizing of some CSs to maintain a minimum service frequency over public transportation lines

Integrating Consumer Flexibility in Smart Grid and Mobility Systems - An Online Optimization and Online Mechanism Design Approach

Consumer flexibility may provide an important lever to align supply and demand in service systems. However, harnessing dispersed flexibility endowments in the presence of self-interested agents requires appropriate incentive structures. This thesis quantifies the potential value of consumers\u27 flexibility in smart grid and mobility systems. In order to include incentives, online optimization approaches are augmented with methods from online mechanism design