Multi Agent Coordination for Demand Management with Energy Generation and Storage

In this paper, we focus on demand side management in consumer collectives with community owned renewable energy generation and storage facilities for effective integration of renewable energy with the existing fossil fuelbased power supply system. The collective buys energy as a group through a central coordinator who also decides about the storage and usage of renewable energy. produced by the collective. Our objective is to design coordination algorithms to minimize the cost of electricity consumption of the consumer collective while allowing the consumers to make their own consumption decisions based on their private consumption constraints and preferences. Minimizing the cost is not only of interest to the consumers but is also socially desirable because it reduces the consumption at times of peak demand (since differential pricing mechanisms like time-of-use pricing is usually used by electricity companies to discourage consumption at times of peak demand). We develop an iterative coordination algorithm in which the coordinator makes the storage decision and shapes the demands of the consumers by designing a virtual price signal for the agents. We prove that our algorithm converges, and it achieves the optimal solution under realistic conditions We also present simulation results based on real world consumption data to quantify the performance of our algorithm

Peer-to-peer and community-based markets: A comprehensive review

The advent of more proactive consumers, the so-called "prosumers", with production and storage capabilities, is empowering the consumers and bringing new opportunities and challenges to the operation of power systems in a market environment. Recently, a novel proposal for the design and operation of electricity markets has emerged: these so-called peer-to-peer (P2P) electricity markets conceptually allow the prosumers to directly share their electrical energy and investment. Such P2P markets rely on a consumer-centric and bottom-up perspective by giving the opportunity to consumers to freely choose the way they are to source their electric energy. A community can also be formed by prosumers who want to collaborate, or in terms of operational energy management. This paper contributes with an overview of these new P2P markets that starts with the motivation, challenges, market designs moving to the potential future developments in this field, providing recommendations while considering a test-case

Collective Action and Social Innovation in the Energy Sector: A Mobilization Model Perspective

This conceptual paper applies a mobilization model to Collective Action Initiatives (CAIs) in the energy sector. The goal is to synthesize aspects of sustainable transition theories with social movement theory to gain insights into how CAIs mobilize to bring about niche-regime change in the context of the sustainable energy transition. First, we demonstrate how energy communities, as a representation of CAIs, relate to social innovation. We then discuss how CAIs in the energy sector are understood within both sustainability transition theory and institutional dynamics theory. While these theories are adept at describing the role energy CAIs have in the energy transition, they do not yet offer much insight concerning the underlying social dimensions for the formation and upscaling of energy CAIs. Therefore, we adapt and apply a mobilization model to gain insight into the dimensions of mobilization and upscaling of CAIs in the energy sector. By doing so we show that the expanding role of CAIs in the energy sector is a function of their power acquisition through mobilization processes. We conclude with a look at future opportunities and challenges of CAIs in the energy transition.This research was conducted under the COMETS (Collective action Models for Energy Transition and Social Innovation) project, funded by the Horizon 2020 Framework Program of the European Commission, grant number 837722

A System Complexity Approach to Swarm Electrification

The study investigates a bottom-up concept for microgrids. Financial analysis is performed through a business model approach to test for viability when replacing a researched energy expenditure baseline in Bangladesh. A literature review compares the approach to current trends in microgrids. A case study of Bangladesh illustrates the potential for building on the existing infrastructure base of solar home systems. Opportunities are identified to improve access to reliable energy through a microgrid approach that aims at community-driven economic and infrastructure development by building on network effects generated through the inclusion of localized economies with strong producer-consumer linkages embedded within larger systems of trade and exchange. The analysed approach involves the linking together of individual stand-alone energy systems to form a microgrid that can eventually interconnect with present legacy infrastructure consisting of national or regional grids. The approach is likened to the concept of swarm intelligence, where each individual node brings independent input to create a conglomerate of value greater than the sum of its parts

Assessing financial and flexibility incentives for integrating wind energy in the grid via agent-based modeling

This article provides an agent-based model of a hypothetical standalone electricity network to identify how the feed-in tariffs and the installed capacity of wind power, calculated in percentage of total system demand, affect the electricity consumption from renewables. It includes the mechanism of electricity pricing on the Day Ahead Market (DAM) and the Imbalance Market (IM). The extra production volumes of Electricity from Renewable Energy Sources (RES-E) and the flexibility of electrical consumption of industries is provided as reserves on the IM. Five thousand simulations were run by using the agent-based model to gather data that were then fit in linear regression models. This helped to quantify the effect of feed-in tariffs and installed capacity of wind power on the consumption from renewable energy and market prices. The consumption from renewable sources, expressed as percentage of total system consumption, increased by 8.17% for every 10% increase in installed capacity of wind power. The sharpest increase in renewable energy consumption is observed when a feed-in tariff of 0.04 €/kWh is provided to the wind farm owners, resulting in an average increase of 9.1% and 5.1% in the consumption from renewable sources while the maximum installed capacity of wind power is 35% and 100%, respectively. The regression model for the annualized DAM prices showed an increase by 0.01 €cents/kWh in the DAM prices for every 10% increase in the installed wind power capacity. With every increase of 0.01 €/kWh in the value of feed-in tariffs, the mean DAM price is lowered as compared to the previous value of the feed-in tariff. DAM prices only decrease with increasing installed wind capacity when a feed-in tariff of 0.04 €/kWh is provided. This is observed because all wind power being traded on DAM at a very cheap price. Hence, no volume of electricity is being stored for availability on IM. The regression models for predicting IM prices show that, with every 10% increase in installed capacity of wind power, the annualized IM price decreases by 0.031 and 0.34 €cents/kWh, when installed capacity of wind power is between 0 and 25%, and between 25 and 100%, respectively. The models also showed that, until the maximum installed capacity of wind power is less than 25%, the IM prices increase when the value of feed-in tariff is 0.01 and 0.04 €/kWh, but decrease for a feed-in tariff of 0.02 and 0.03 €/kWh. When installed capacity of wind power is between 25 and 100%, increasing feed-in tariffs to the value of 0.03 €/kWh result in lowering the mean IM price. However, at 0.04 €/kWh, the mean IM price is higher, showing the effect of no storage reserves being available on IM and more expensive reserves being engaged on the IM. The study concludes that the effect of increasing installed capacity of wind power is more significant on increasing consumption of renewable energy and decreasing the DAM and IM prices than the effect of feed-in tariffs. However, the effect of increasing values of both factors on the profit of RES-E producers with storage facilities is not positive, pointing to the need for customized rules and incentives to encourage their market participation and investment in storage facilities

Agent-based homeostatic control for green energy in the smart grid

With dwindling non-renewable energy reserves and the adverse effects of climate change, the development of the smart electricity grid is seen as key to solving global energy security issues and to reducing carbon emissions. In this respect, there is a growing need to integrate renewable (or green) energy sources in the grid. However, the intermittency of these energy sources requires that demand must also be made more responsive to changes in supply, and a number of smart grid technologies are being developed, such as high-capacity batteries and smart meters for the home, to enable consumers to be more responsive to conditions on the grid in real-time. Traditional solutions based on these technologies, however, tend to ignore the fact that individual consumers will behave in such a way that best satisfies their own preferences to use or store energy (as opposed to that of the supplier or the grid operator). Hence, in practice, it is unclear how these solutions will cope with large numbers of consumers using their devices in this way. Against this background, in this paper, we develop novel control mechanisms based on the use of autonomous agents to better incorporate consumer preferences in managing demand. These agents, residing on consumers' smart meters, can both communicate with the grid and optimise their owner's energy consumption to satisfy their preferences. More specifically, we provide a novel control mechanism that models and controls a system comprising of a green energy supplier operating within the grid and a number of individual homes (each possibly owning a storage device). This control mechanism is based on the concept of homeostasis whereby control signals are sent to individual components of a system, based on their continuous feedback, in order to change their state so that the system may reach a stable equilibrium. Thus, we define a new carbon-based pricing mechanism for this green energy supplier that takes advantage of carbon-intensity signals available on the internet in order to provide real-time pricing. The pricing scheme is designed in such a way that it can be readily implemented using existing communication technologies and is easily understandable by consumers. Building upon this, we develop new control signals that the supplier can use to incentivise agents to shift demand (using their storage device) to times when green energy is available. Moreover, we show how these signals can be adapted according to changes in supply and to various degrees of penetration of storage in the system. We empirically evaluate our system and show that, when all homes are equipped with storage devices, the supplier can significantly reduce its reliance on other carbon-emitting power sources to cater for its own shortfalls. By so doing, the supplier reduces the carbon emission of the system by up to 25% while the consumer reduces its costs by up to 14.5%. Finally, we demonstrate that our homeostatic control mechanism is not sensitive to small prediction errors and the supplier is incentivised to accurately predict its green production to minimise costs

Shared PV Production in Energy Communities and Buildings Context

19th International Conference on Renewable Energies and Power Quality (ICREPQ'21), Almeria (Spain), 28th to 30th July 2021Across Europe, householders have taken the opportunity to produce their electricity, helping them to reduce their electricity bill as well as reducing carbon emissions, by installing rooftop Photovoltaic Panels (PV) on their buildings. New adequate business models are needed for improving the sharing of PV between consumers in a community. Both technical and economic aspects should be considered in a clear way for consumers to understand and benefit from the community. In this paper, an overview of energy communities is provided to support the innovative PV sharing models for buildings, which are proposed in a way to be clear for community members. The developed concepts are supported by the formulation of the optimization problem to be solved by the community manager.This work has received funding from FEDER Funds through COMPETE program and from National Funds through FCT under the project BENEFICE–PTDC/EEIEEE/29070/2017 and UIDB/00760/2020 under CEECIND/02814/2017 grant.info:eu-repo/semantics/publishedVersio