The advances in distributed renewable generation technologies in recent years have started to cause load balancing issues in power networks. Distributed energy storage (ES) systems, although seen as a tool to mitigate the stress on local networks, tend to be operated only to minimize the energy cost of their direct owner. In this paper, cooperative game theory is used to construct an energy grand coalition, in which ES system operations are optimized to minimize the coalitional energy cost. Case studies then show that forming energy coalitions is effective in reducing the variability of a local network load profile. The resulting cooperative game is mathematically proven to be balanced, which means the energy cost savings from the cooperative ES operation can be allocated to the players within the energy grand coalition in a manner that disincentivizes them from exiting from the grand coalition to form smaller coalitions.

/pdf/incentivizing-prosumer-coalitions-with-energy-management-3jutpfhceo.pdf

Incentivizing Prosumer Coalitions With Energy Management Using Cooperative Game Theory

Community-driven energy initiatives are seen as important drivers of the energy transition. So far these initiatives focused mainly on energy generation and conservation. Recently however, some initiatives started to adopt smart grid technologies like Virtual Power Plants (VPP) which enables them to become involved in the distribution, trading and management of energy. By adopting a multiple-methods approach consisting of literature reviews on community energy and Virtual Power Plant, action research, semi-structured interviews and by mobilizing Family Resemblance, this article explores the community-based Virtual Power Plants as a novel model for energy provision. We identified five building blocks that together form a cVPP: the community involved, the community-logic under which the project operates, the portfolio of distributed energy resources aggregated and controlled by an ICT control architecture, and the roles communities can collectively play in the energy system by means of cVPP. Three practical cases in Ireland, Belgium and the Netherlands highlight the diversity of cVPPs, that results from different choices and trade-offs made by various communities in diverse institutional contexts. Applying the cVPP conceptualization made visible that the three cVPPs had to comply with the incumbent energy system, making it difficult to play the preferred roles in the energy system, operate on the scale of their community and to keep their own needs and values center stage.

Community energy meets smart grids: Reviewing goals, structure, and roles in Virtual Power Plants in Ireland, Belgium and the Netherlands

The fast growth of technologies most of which depend on natural sources of energy has resulted in a huge consumption of fossil fuels. In this regard, many solutions have been suggested to alleviate the side effects such as air pollution and global warming. Among these solutions, mobile storages like electric vehicles (EVs) and renewable generations, have grown significantly due to being more applicable. But uncoordinated operation and uncertain nature of these distributed energy resources (DERs) can bring forward new challenges and issues to the operators of power system. Thus, in many cases it is more efficient to co-operate them in a hybrid system. In this study, we address a virtual power plant (VPP) that aggregates the EVs charging and discharging power into electricity markets, day-ahead (DA) energy and reserve, while enjoying wind power generation capacity. Not only we consider uncertainty of electricity market prices and the amount of energy produced by wind generation but also we present a novel EV uncertainty modelling in which we introduce a new facet that incorporates all the uncertain parameters of these vehicles into the whole stochastic optimization model along with other uncertainty sources. The proposed method can be utilized in case of a VPP that has wind generation and parking-lots accommodating EVs to optimally schedule its assets prior to participating in the electricity markets. The theoretical approach in developing the proposed self-scheduling model and its applicability is verified through several numerical simulations.

A hybrid storage-wind virtual power plant (VPP) participation in the electricity markets: A self-scheduling optimization considering price, renewable generation, and electric vehicles uncertainties

Solving the issue of energy security for geographical islands presents a one-of-a-kind problem that has to be tackled from multiple sides and requires an interdisciplinary approach that transcends just technical and social aspects. With many islands suffering in terms of limited and costly energy supply due to their remote location, providing a self-sustainable energy system is of utmost importance for these communities. In order to improve upon the status quo, novel solutions and projects aimed at increasing sustainability not only have to consider optimal utilization of renewable energy potentials in accordance with local conditions, but also must include active community participation. This paper analyzes both of these aspects for island communities and brings them together in an optimization scenario that is utilized to determine the relationship between supposed demand flexibility levels and achievable savings in a setting with variable renewable generation. The results, specifically discussed for a use case with real-world data for the La Graciosa island in Spain, show that boosting community participation and thus unlocking crucial demand flexibility, can be used as a powerful tool to augment novel generation technologies with savings from flexibility at around 7.5% of what is achieved purely by renewable sources.

https://www.mdpi.com/1996-1073/13/13/3386/pdf

Towards Self-Sustainable Island Grids through Optimal Utilization of Renewable Energy Potential and Community Engagement

Virtual power plants (VPPs) are promising solutions to address the decarbonization and energy efficiency goals in the smart energy grid. They assume the coordination of local energy resources such as energy generation, storage, and consumption. They are used to tackle problems brought by the stochastic nature of renewable energy, lack of energy storage devices, or insufficient local energy flexibility on the demand side. VPP modeling, management, and optimization are open to research problems that should consider, on one side, the local constraints in the operation of the energy resources and power flows and the energy grid’s sustainability objectives on the other side. There are multiple goals to create a VPP, such as to deliver energy services on a market or to the grid operator, to operate a microgrid in autonomy decoupled from the main grid, or to sustain local energy communities. In this paper, we present the results of a narrative review carried out on the domain of VPP optimization for the local energy grid integration. We have defined a search strategy that considers highly rated international databases (i.e., Elsevier, IEEE, and MDPI) in a six-year timeframe and applied objective inclusion/exclusion criteria for selecting articles and publications for the review; 95 articles have been analyzed and classified according to their objectives and solutions proposed for optimizing VPP integration in smart grids. The results of the study show that VPP concepts and applications are well addressed in the research literature, however, there is still work to be done on: engaging prosumers and citizens in such a virtual organization, developing heuristics to consider a wider range of local and global constraints and non-energy vectors, and to decentralize and make transparent the services delivery and financial settlement towards community members. This study can help researchers to understand the current directions for VPP integration in smart grids. As a next step we plan to further analyze the open research directions related to this problem and target the development of innovative solutions to allow the integration of multi-energy assets and management of cross energy sector services in energy communities.

/pdf/virtual-power-plant-optimization-in-smart-grids-a-narrative-135gwna7.pdf

Virtual Power Plant Optimization in Smart Grids: A Narrative Review

Provision of security involves protecting lives and properties, and properties in this context include data and services. This paper investigates the impact of cyber-attacks on load scheduling applications by simulating various possible modes for these attacks while observing possible effects on the users. The attack modes used are in the form of denial of service (DoS) and phishing attacks whereby the attacker is able to interfere with data intake to the Home Energy Management Systems (HEMS) or a modification of critical data to the HEMS. The dynamic pricing information and load profile data is the target here although other types of data utilized by the central controller for load scheduling purposes can also be targeted. The test-bed uses load scheduling applications based on genetic algorithm optimization. Results show the impact on optimized load profiles and how they can discourage active demand response participation if such attacks are not properly managed.

/pdf/investigating-the-impacts-of-cyber-attacks-on-pricing-data-1kzuze75gi.pdf

Investigating the Impacts of Cyber-Attacks on Pricing Data of Home Energy Management Systems in Demand Response Programs

Technological advancement on the electricity grid has focused on maximizing its use. This has led to the introduction of energy storage. Energy storage could be used to provide both peak and off-peak services to the grid. Recent work on the use of small units of energy storage like battery has proposed the vehicle to grid system. It is propose in this work to have energy storage device embedded inside the house of the energy consumer. In such a system, consumers with battery energy storage can be aggregated in to a community virtual power plant. In this paper, an optimized energy resource allocation algorithm is presented for a virtual power plant using genetic algorithm. The results show that it is critical to have a pricing scheme that help achieve goals for grid, virtual power plant, and consumers.

/pdf/evaluation-of-community-virtual-power-plant-under-various-28e1841j90.pdf

Evaluation of community virtual power plant under various pricing schemes

Recently, significant advances in renewable energy generation have made it possible to consider consumers as prosumers. However, with increase in embedded generation, storage of electrical energy in batteries, flywheels and supercapacitors has become important so as to better utilize the existing grid by helping smooth the peaks and troughs of renewable electricity generation, and also of demand. This has led to the possibility of controlling the times when stored energy from these storage units is fed back to the grid. In this paper we look at how energy resource sharing is achieved if these storage units are part of a virtual power plant. In a virtual power plant, these storage units become energy resources that need to be optimally scheduled over time so as to benefit both prosumer and the grid supplier. In this paper, a smart energy resources allocation algorithm is presented for a virtual power plants using genetic algorithms. It is also proposed that the cause of battery depreciation be accounted for in the allocation of discharge rates. The algorithm was tested under various pricing scenarios, depreciation cost, as well as constraint. The results are presented and discussed. Conclusions were drawn, and suggestion for further work was made.

/pdf/investigation-of-an-optimized-energy-resource-allocation-4thxejj7dp.pdf

Investigation of an optimized energy resource allocation algorithm for a community based virtual power plant

The introduction of smart metering has brought more detailed information on the actual load profile of a house. With the ability to measure, comes the desire to control the load profile. Furthermore, advances in renewable energy have made the consumer to become supplier, known as Prosumer, who therefore also becomes interested in the detail of his load, and also his energy production. With the lowering cost of smart plugs and other automation units, it has become possible to schedule electrical appliances. This makes it possible to adjust the load profiles of houses. However, without a market in the demand side, the use of load profile modification techniques are unlikely to be adapted by consumers on the long term. In this research, we will be presenting work on scheduling of energy appliances to modify the load profiles within a market environment. The paper will review the literature on algorithms used in scheduling of appliances in residential areas. Whilst many algorithms presented in the literature show that scheduling of appliances is feasible, many issues arise with respect to user interaction, and hence adaptation. Furthermore, the criteria used to evaluate the algorithms is often related only to reducing energy consumption, and hence CO2. Whilst this a key factor, it may not necessarily meet the demands of the consumer. In this paper we will be presenting work on a novel genetic algorithm that will optimize the load profile while taking into account user participation indices. A novel measure of the comfort of the customer, derived from the standard deviation of the load profile, is proposed in order to encourage the customer to participate more actively in demand response programs. Different scenarios will also be tested.

/pdf/novel-genetic-algorithm-for-scheduling-of-appliances-to848s35l0.pdf

Novel genetic algorithm for scheduling of appliances

Energy consumers oftentimes suffer some element of discomfort associated with the implementation of demand response programs as they aim to follow a suggested energy consumption profile generated from scheduling algorithms for the purpose of optimizing grid performance. This is because people naturally do not like to be told what to do or when to use their appliances. Although advances in renewable energy have made the consumer to also become energy supplier, who can actively cash in at times of the day when energy cost is high to either sell excess energy generated or consume it internally if required, thereby nullifying the adverse effect of this discomfort. But a majority of consumers still rely wholly on the supply from the grid. This impact on users' comfort who are active participants in demand response programs was investigated and ways to minimizing load scheduling discomfort was sought in order to encourage user participation.

/pdf/investigating-the-impact-of-discomfort-in-load-scheduling-26034q0yxq.pdf

Ugonna R. Anuebunwa

Papers

Investigating the Impacts of Cyber-Attacks on Pricing Data of Home Energy Management Systems in Demand Response Programs

Evaluation of community virtual power plant under various pricing schemes

Investigation of an optimized energy resource allocation algorithm for a community based virtual power plant

Novel genetic algorithm for scheduling of appliances

Investigating the impact of discomfort in load scheduling using genetic algorithm