scholarly journals Optimal Simulation of Three Peer to Peer (P2P) Business Models for Individual PV Prosumers in a Local Electricity Market Using Agent-Based Modelling

Buildings ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 138 ◽  
Author(s):  
Marco Lovati ◽  
Xingxing Zhang ◽  
Pei Huang ◽  
Carl Olsmats ◽  
Laura Maturi
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Business Models ◽  
Energy System ◽  
Peer To Peer ◽  
Positive Energy ◽  
Financial Flows ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Self Sufficiency

Solar photovoltaic (PV) is becoming one of the most significant renewable sources for positive energy district (PED) in Sweden. The lack of innovative business models and financing mechanisms are the main constraints for PV’s deployment installed in local communities. This paper therefore proposes a peer-to-peer (P2P) business model for 48 individual building prosumers with PV installed in a Swedish community. It considers energy use behaviour, electricity/financial flows, ownerships and trading rules in a local electricity market. Different local electricity markets are designed and studied using agent-based modelling technique, with different energy demands, cost–benefit schemes and financial hypotheses for an optimal evaluation. This paper provides an early insight into a vast research space, i.e., the operation of an energy system through the constrained interaction of its constituting agents. The agents (48 households) show varying abilities in exploiting the common PV resource, as they achieve very heterogeneous self-sufficiency levels (from ca. 15% to 30%). The lack of demand side management suggests that social and lifestyle differences generate huge impacts on the ability to be self-sufficient with a shared, limited PV resource. Despite the differences in self-sufficiency, the sheer energy amount obtained from the shared PV correlates mainly with annual cumulative demand.

Agent-Based Model of a Blockchain Enabled Peer-to-Peer Energy Market: Application for a Neighborhood Trial in Perth, Australia

Smart Cities ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 1072-1099 ◽  
Author(s):  
Jacob G. Monroe ◽  
Paula Hansen ◽  
Matthew Sorell ◽  
Emily Zechman Berglund
Keyword(s):  
Electricity Market ◽  
Energy System ◽  
Peer To Peer ◽  
Energy Market ◽  
Energy Trading ◽  
Agent Based ◽  
Trading Systems ◽  
Market Structures ◽  
Trade Offs ◽  
The Impact

The transfer of market power in electric generation from utilities to end-users spurred by the diffusion of distributed energy resources necessitates a new system of settlement in the electricity business that can better manage generation assets at the grid-edge. A new concept in facilitating distributed generation is peer-to-peer energy trading, where households exchange excess power with neighbors at a price they set themselves. However, little is known about the effects of peer-to-peer energy trading on the sociotechnical dynamics of electric power systems. Further, given the novelty of the concept, there are knowledge gaps regarding the impact of alternative electricity market structures and individual decision strategies on neighborhood exchanges and market outcomes. This study develops an empirical agent-based modeling (ABM) framework to simulate peer-to-peer electricity trades in a decentralized residential energy market. The framework is applied for a case study in Perth, Western Australia, where a blockchain-enabled energy trading platform was trialed among 18 households, which acted as prosumers or consumers. The ABM is applied for a set of alternative electricity market structures. Results assess the impact of solar generation forecasting approaches, battery energy storage, and ratio of prosumers to consumers on the dynamics of peer-to-peer energy trading systems. Designing an efficient, equitable, and sustainable future energy system hinges on the recognition of trade-offs on and across, social, technological, economic, and environmental levels. Results demonstrate that the ABM can be applied to manage emerging uncertainties by facilitating the testing and development of management strategies.

scholarly journals Agent Based Modelling of a Local Energy Market: A Study of the Economic Interactions between Autonomous PV Owners within a Micro-Grid

Buildings ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 160
Author(s):  
Marco Lovati ◽  
Pei Huang ◽  
Carl Olsmats ◽  
Da Yan ◽  
Xingxing Zhang
Keyword(s):  
Positive Energy ◽  
Pv System ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Pv Systems ◽  
Self Sufficiency ◽  
Improvement Potential ◽  
Local Grid ◽  
Modelling Environment ◽  
Residential Electric Demand

Urban Photovoltaic (PV) systems can provide large fractions of the residential electric demand at socket parity (i.e., a cost below the household consumer price). This is obtained without necessarily installing electric storage or exploiting tax funded incentives. The benefits of aggregating the electric demand and renewable output of multiple households are known and established; in fact, regulations and pilot energy communities are being implemented worldwide. Financing and managing a shared urban PV system remains an unsolved issue, even when the profitability of the system as a whole is demonstrable. For this reason, an agent-based modelling environment has been developed and is presented in this study. It is assumed that an optimal system (optimized for self-sufficiency) is shared between 48 households in a local grid of a positive energy district. Different scenarios are explored and discussed, each varying in number of owners (agents who own a PV system) and their pricing behaviour. It has been found that a smaller number of investors (i.e., someone refuse to join) provokes an increase of the earnings for the remaining investors (from 8 to 74% of the baseline). Furthermore, the pricing strategy of an agent shows improvement potential without knowledge of the demand of others, and thus it has no privacy violations.

scholarly journals Prosumers’ Behavior under a Regulation That Encourages Strict Self-Sufficiency. The Case of Spanish Photovoltaic Micro-Generation

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1114
Author(s):  
Pere Mir-Artigues ◽  
Pablo del Río
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Specific Form ◽  
Economic Behavior ◽  
Distribution Grid ◽  
Self Sufficiency ◽  
Regulatory Limits ◽  
Regulatory Measures ◽  
Self Production ◽  
Municipal Level

The reduction of equipment costs encourages the diffusion of photovoltaic micro-generation, however, proper regulatory measures should be implemented to facilitate self-production dissemination and to promote the emergence of new electricity markets which integrate prosumers. The specific form of these markets will depend on the level of prosumers’ self-sufficiency and the type of grid to which they will be connected. Unfortunately, Spain has been an example of resistance to micro-generation deployment. However, some things have started to change recently, albeit only to a certain extent. This article explains the key elements of the latest regulation of photovoltaic micro-generation in Spain and, through a stylized model, describes the economic behavior of prosumers in such a regulatory framework. It is concluded that this regulation only encourages prosumer plants which are strictly focused on self-sufficiency because it discourages exports and limits capacities and this regulation discourages the smart renewal of the distribution grid because it prevents prosumers from participating in the electricity market. It is recommended that the aforementioned regulatory limits be removed and pilot experiences for the market participation of prosumers be promoted by creating the appropriate technical and regulatory conditions, for example, at the municipal level.

scholarly journals Credible Peer-to-Peer Trading with Double-Layer Energy Blockchain Network in Distributed Electricity Markets

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1815
Author(s):  
Longze Wang ◽  
Yu Xie ◽  
Delong Zhang ◽  
Jinxin Liu ◽  
Siyu Jiang ◽  
...  
Keyword(s):  
Double Layer ◽  
Electricity Markets ◽  
Electricity Market ◽  
Peer To Peer ◽  
Malicious Nodes ◽  
Energy Trading ◽  
Contract Performance ◽  
Trading Model ◽  
Consensus Information ◽  
Available Information

Blockchain-based peer-to-peer (P2P) energy trading is one of the most viable solutions to incentivize prosumers in distributed electricity markets. However, P2P energy trading through an open-end blockchain network is not conducive to mutual credit and the privacy protection of stakeholders. Therefore, improving the credibility of P2P energy trading is an urgent problem for distributed electricity markets. In this paper, a novel double-layer energy blockchain network is proposed that stores private trading data separately from publicly available information. This blockchain network is based on optimized cross-chain interoperability technology and fully considers the special attributes of energy trading. Firstly, an optimized ring mapping encryption algorithm is designed to resist malicious nodes. Secondly, a consensus verification subgroup is built according to contract performance, consensus participation and trading enthusiasm. This subgroup verifies the consensus information through the credit-threshold digital signature. Thirdly, an energy trading model is embedded in the blockchain network, featuring dynamic bidding and credit incentives. Finally, the Erenhot distributed electricity market in China is utilized for example analysis, which demonstrates the proposed method could improve the credibility of P2P trading and realize effective supervision.

Leveraging peer-to-peer farmer learning to facilitate better strategies in smallholder dairy husbandry

2020 ◽  
pp. 105971232097136
Author(s):  
Devotha G Nyambo ◽  
Edith T Luhanga ◽  
Zaipuna O Yonah ◽  
Fidalis DN Mujibi ◽  
Thomas Clemen
Keyword(s):  
Milk Yield ◽  
Peer Learning ◽  
Peer To Peer ◽  
Inexpensive Method ◽  
Dairy Farmers ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Farmer Groups ◽  
Using Data ◽  
New Strategies

Peer-to-peer learning paradigm is seldom used in studying how farmers can increase yield. In this article, agent-based modelling has been applied to study the chances of dairy farmers increasing annual milk yield by learning better farming strategies from each other. Two sets of strategies were considered; in one set ( S), each farmer agent would possess a number of farming strategies based on their knowledge, and in a second set [Formula: see text], farmer agents would possess farming strategies that they have adopted from their peers. Regression models were used to determine litres of milk that could be produced whenever new strategies were applied. By using data from Ethiopia and Tanzania, 28 and 25 determinants for increase in milk yield were fitted for the two countries, respectively. There was a significant increase in average milk yield as the farmer agents interacted and updated their [Formula: see text]– from baseline data, average milk yield of 12.7 ± 4.89 and 13.62 ± 4.47 to simulated milk yield average of 17.57 ± 0.72 and 20.34 ± 1.16 for Tanzania and Ethiopia, respectively. The peer-to-peer learning approach details an inexpensive method manageable by the farmers themselves. Its implementation could range from physical farmer groups to online interactions.

scholarly journals Using Agent-Based Models to Generate Transformation Knowledge for the German Energiewende—Potentials and Challenges Derived from Four Case Studies

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6133
Author(s):  
Georg Holtz ◽  
Christian Schnülle ◽  
Malcolm Yadack ◽  
Jonas Friege ◽  
Thorben Jensen ◽  
...  
Keyword(s):  
Case Studies ◽  
Model Development ◽  
Stakeholder Involvement ◽  
Energy System ◽  
Agent Based Models ◽  
Agent Based ◽  
Industrial Economy ◽  
Agent Based Modelling ◽  
Model Studies ◽  
Phase Out

The German Energiewende is a deliberate transformation of an established industrial economy towards a nearly CO2-free energy system accompanied by a phase out of nuclear energy. Its governance requires knowledge on how to steer the transition from the existing status quo to the target situation (transformation knowledge). The energy system is, however, a complex socio-technical system whose dynamics are influenced by behavioural and institutional aspects, which are badly represented by the dominant techno-economic scenario studies. In this paper, we therefore investigate and identify characteristics of model studies that make agent-based modelling supportive for the generation of transformation knowledge for the Energiewende. This is done by reflecting on the experiences gained from four different applications of agent-based models. In particular, we analyse whether the studies have improved our understanding of policies’ impacts on the energy system, whether the knowledge derived is useful for practitioners, how valid understanding derived by the studies is, and whether the insights can be used beyond the initial case-studies. We conclude that agent-based modelling has a high potential to generate transformation knowledge, but that the design of projects in which the models are developed and used is of major importance to reap this potential. Well-informed and goal-oriented stakeholder involvement and a strong collaboration between data collection and model development are crucial.

scholarly journals Pricing decisions in peer-to-peer and prosumer-centred electricity markets: Experimental analysis in Germany and the United Kingdom

2021 ◽  
Author(s):  
Ulf J.J. Hahnel ◽  
Michael James Fell
Keyword(s):  
United Kingdom ◽  
Citizen Participation ◽  
Electricity Markets ◽  
Electricity Market ◽  
Supply And Demand ◽  
Energy Transition ◽  
Active Participation ◽  
Peer To Peer ◽  
Political Orientation ◽  
The United Kingdom

Prosumer-centred electricity market models such as peer-to-peer communities can enable optimized supply and demand of locally generated electricity as well as an active participation of citizens in the energy transition. An important element of active participation is the improved ability of community members to identify and choose who they transact with in a much more granular way than is usual. Despite this key novelty and the social core of prosumer-centred markets, little is known about how citizens would trade with different actors involved in the system. Here, we report a preregistered cross-national experiment in which we investigated individual trading preferences in a peer-to-peer community, including a variety of private and non-private trading actors. Our data from the United Kingdom (n=441) and Germany (n=440) shows that set buying and selling prices strongly vary, pointing to three systematically different trading strategies that individuals apply as a function of involved trading actor. Findings moreover reveal that trading decisions are determined by individuals’ political orientation, place attachment, and climate change beliefs as well as individual differences in trust in the involved trading actor. Finally, our results illustrate high consistency in trading preferences across nations. However, nation-level differences emerged when decisions were made publicly visible, emphasising the need to consider context-effects in peer-to-peer system design. Our results have implications for the development of prosumer-centred energy models and the design of interventions to increase citizen participation across national contexts.

Smart Infrastructure: Innovative Energy Technology, Climate Mitigation, and Consumer Protection in Australia and Germany

2018 ◽  
Author(s):  
Lee Godden ◽  
Anne Kallies
Keyword(s):  
Climate Change ◽  
Case Studies ◽  
Consumer Protection ◽  
Electricity Markets ◽  
Business Models ◽  
Cost Savings ◽  
Energy System ◽  
Climate Mitigation ◽  
Smart Meters ◽  
Smart Infrastructure

‘Smart infrastructure’, such as smart meters, are innovative, information-based energy technologies designed to promote systemic energy efficiency, cost savings, and to transition energy markets toward sustainable outcomes, including reducing climate change impacts. Smart meters promise innovation in electricity markets–as an enabler of demand-side services and a more distributed energy system. The chapter examines three case studies of legal reform for smart meter introduction in Australia and Germany. It concludes that the realization of the innovation promise of smart infrastructure requires the legal system to address consumer-oriented social and economic changes. While legal responses are growing in sophistication, significant questions around consumer protection remain, although Germany emphasizes consumer privacy more than Australian case studies. Finally, Germany most closely links innovation to climate change and electricity system transitions, whereas, increasingly, Australian policies emphasize the consumer benefits and innovation in the business models for electricity distribution.

scholarly journals Agent-Based Modelling of Urban District Energy System Decarbonisation – A Systematic Literature Review

2021 ◽  
Author(s):  
Ardak Akhatova ◽  
Lukas Kranzl ◽  
Fabian Schipfer ◽  
Charitha Buddhika Heendeniya
Keyword(s):  
Urban Areas ◽  
Energy Transition ◽  
Energy System ◽  
Social Characteristics ◽  
Solar Panels ◽  
Urban District ◽  
Agent Based ◽  
Agent Based Modelling ◽  
District Energy ◽  
Industrial Energy

There is an increased interest in the district-scale energy transition within interdisciplinary research community. Agent-based modelling presents a suitable approach to address variety of questions related to policies, technologies, processes, and the different stakeholder roles that can foster such transition. This state-of-the-art review focuses on the application of agent-based modelling for exploring policy interventions that facilitate the decarbonisation (i.e., energy transition) of districts and neighbourhoods while considering stakeholders’ social characteristics and interactions. We systematically select and analyse peer-reviewed literature and discuss the key modelling aspects, such as model purpose, agents and decision-making logic, spatial and temporal aspects, and empirical grounding. The analysis reveals that the most established agent-based models’ focus on innovation diffusion (e.g., adoption of solar panels) and dissemination of energy-saving behaviour among a group of buildings in urban areas. We see a considerable gap in exploring the decisions and interactions of agents other than residential households, such as commercial and even industrial energy consumers (and prosumers). Moreover, measures such as building retrofits and conversion to district energy systems involve many stakeholders and complex interactions between them that up to now have hardly been represented in the agent-based modelling environment.

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