Fig 1 - uploaded by Jaysson Guerrero
Content may be subject to copyright.
Time-of-use tariff (ToU) and feed-in-tariff (FiT)
Source publication
Over the past years, distributed energy resources (DER) have been the object of many studies, which recognise and establish their emerging role in the future of power systems. However, the implementation of many scenarios and mechanism are still challenging. This paper provides an overview of a local energy market and explores the approaches in whi...
Context in source publication
Context 1
... profiles, with one minute resolution, are based on the CREST Demand Model [11]. In Fig 1 are depicted the time-of-use tariff (ToU) and feed-in-tariff (FiT) used in our model. Since ZIP traders improved their performance when the maximum and minimum constraints are defined, we use the values of import and export tariffs through the day to define L max and L min . ...
Similar publications
A reliable solution for determining the effect of a group of contingencies on a power system is to simulate all of them through load flow. It is impossible to simulate any detailed cases through a complete AC load flow solution because of the high number of possible contingencies. For this reason, system dimension reduction methods, which are based...
Citations
... Instead, this information can be transmitted directly between participants, encrypted, and protected to ensure privacy. However, in a decentralized market, the utility grid company may face difficulties in scheduling resources due to the absence of a centralized coordinator, resulting in less efficient operation of the distribution system [23]. At the same time, individual participants are more concerned with their interests and may neglect the welfare of the whole community or collective. ...
After a century of relative stability in the electricity sector, the widespread adoption of distributed energy resources, along with recent advancements in computing and communication technologies, has fundamentally altered how energy is consumed, traded, and utilized. This change signifies a crucial shift as the power system evolves from its traditional hierarchical organization to a more decentralized approach. At the heart of this transformation are innovative energy distribution models, like peer-to-peer (P2P) sharing, which enable communities to collaboratively manage their energy resources. The effectiveness of P2P sharing not only improves the economic prospects for prosumers, who generate and consume energy, but also enhances energy resilience and sustainability. This allows communities to better leverage local resources while fostering a sense of collective responsibility and collaboration in energy management. However, there is still no extensive implementation of such sharing models in today’s electricity markets. Research on distributed energy P2P trading is still in the exploratory stage, and it is particularly important to comprehensively understand and analyze the existing distributed energy P2P trading market. This paper contributes with an overview of the P2P markets that starts with the network framework, market structure, technical approach for trading mechanism, and blockchain technology, moving to the outlook in this field.
... Figure 3b illustrates the functioning of a decentralized P2P energy trading market. However, since peers work independently in such a market, maximizing the community's social welfare can be difficult, leading to lower efficiency in community trading (Guerrero et al. [2017]). ...
To achieve desired carbon emission reductions, integrating renewable generation and accelerating the adoption of peer-to-peer energy trading is crucial. This is especially important for energy-intensive farming, like dairy farming. However, integrating renewables and peer-to-peer trading presents challenges. To address this, we propose the Multi-Agent Peer-to-Peer Dairy Farm Energy Simulator (MAPDES), enabling dairy farms to participate in peer-to-peer markets. Our strategy reduces electricity costs and peak demand by approximately 30% and 24% respectively, while increasing energy sales by 37% compared to the baseline scenario without P2P trading. This demonstrates the effectiveness of our approach.
... Fig. 3(b) illustrates the functioning of a decentralized P2P energy trading market. However, since peers work independently in such a market, maximizing the community's social welfare can be difficult, leading to lower efficiency in community trading (Guerrero et al., 2017). • Distributed/ Community Market: The distributed or community P2P energy-sharing market combines centralized and decentralized energy market features. ...
Energy networks around the world have experienced a significant increase in the amount of distributed generation. This decentralization of energy markets has led to a surge of interest in Peer-to-Peer energy trading between individuals. There are many challenges associated with Peer-to-Peer energy trading, e.g. behaviour modelling, decision making and optimization. Multi-agent systems is a prominent subfield of Artificial Intelligence research that is very effective for these types of problems. This research aims to provide a detailed survey of recent advances in the application of multi-agent systems in Peer-to-Peer energy trading. The challenges encountered in implementing these systems are discussed, e.g. agent learning, privacy, and computational power. The primary advantages of multi-agent systems for Peer-to-Peer energy trading reported in the literature are: improved efficiency of renewable utilization, reduced transaction costs, and scalability. Current challenges and future directions of research are also outlined.
... In contrast, vendor platforms aim to help distributed energy resource (DER) vendors enhance the value of their products. Battery storage and photovoltaic (PV) panel innovations offered by specific battery systems and PV panel vendors can reduce the charging cost for electric vehicle fleets and the functional loss related to high-voltage electricity [91,93]. ...
The emergence of distributed energy has led to a change in the role of the consumer in the traditional sense over the past decade. The proliferation of emerging generators and distributors has created opportunities for a more decentralised and open energy market. In particular, the emergence of peer-to-peer (P2P) energy trading models, challenged by the surge in demand for sustainable energy, has eliminated the need for intermediaries in energy transactions between consumers, producers, and sellers. Due to the great promise of sustainable energy, both in terms of its contribution to the environment and production costs, this paper reviews a number of well-known P2P energy trading platforms to understand what makes P2P energy trading platforms more functional. As a result, areas for consideration were identified and grouped into five themes: (1) set-up, (2) market, (3) information, (4) price, and (5) regulation.
... The purpose is to select the most suitable time to start the EV charging activity so that the charging cost is minimized. The ToU [28] and RTP [29] are shown in Figure 7 in units of USD/kWh. mid-peak occurs when the energy demand is moderate. ...
... The purpose is to select the most suitable time to start the EV charging activity so that the charging cost is minimized. The ToU [28] and RTP [29] are shown in Figure 7 in units of USD/kWh. ...
Advancements in technology and awareness of energy conservation and environmental protection have increased the adoption rate of electric vehicles (EVs). The rapidly increasing adoption of EVs may affect grid operation adversely. However, the increased integration of EVs, if managed appropriately, can positively impact the performance of the electrical network in terms of power losses, voltage deviations and transformer overloads. This paper presents a two-stage multi-agentbased scheme for the coordinated charging scheduling of EVs. The first stage uses particle swarm optimization (PSO) at the distribution network operator (DNO) level to determine the optimal power allocation among the participating EV aggregator agents to minimize power losses and voltage deviations, whereas the second stage at the EV aggregator agents level employs a genetic algorithm
(GA) to align the charging activities to achieve customers’ charging satisfaction in terms of minimum charging cost and waiting time. The proposed method is implemented on the IEEE-33 bus network connected with low-voltage nodes. The coordinated charging plan is executed with the time of use (ToU) and real-time pricing (RTP) schemes, considering EVs’ random arrival and departure with two
penetration levels. The simulations show promising results in terms of network performance and overall customer charging satisfaction.
... By solving the optimisation problem of the integrated operation of natural gas and power networks, Equations (1-20), the energy consumption pattern of microgrids P dem;DR i;t , the charging/discharging cycle of the microgrid's energy storage systems P T ot;dec i;t and gas turbines power generation schedule P gt i;t are determined. In the proposed method, we assume that the customers are ZIP traders [52]. A ZIP trader only trades for a profit and can be expected to mimic the behaviour of human traders quite well in the stock market. ...
... In addition, the offer/bid price and the amount of electricity to be bought/sold are determined separately rather than simultaneously at one step. We selected this framework based on Ref. [52], which is applicable but not optimal. A better (even optimum) bidding tactic for a CDA-based P2P market framework stays to be investigated, but that is not the scope of this study. ...
Peer‐to‐peer (P2P) energy trading is a new technology for integrating distributed energy resources (DERs) into the power system. A P2P market allows direct energy trading between end‐users, enables local power and energy equilibrium and supports power grid operations. As a common DER, gas‐fired power plants are employed to deal with the intermittency of the power system due to their flexible characteristics. Therefore, the intermittency in the power system transmits to the gas system through the gas‐fired power plants, which makes the operation of these systems even more interdependent and cost‐effective. This paper proposes a market‐based two‐stage framework for the integrated operation of power and natural gas grids taking into account demand response and both network constraints. In the first stage (scheduling stage), the MINLP‐based optimisation approach is used for the optimal scheduling of two energy carriers considering AC power flow and gas hydraulic calculations for the next 24 h. Then, in the second stage, the continuous double auction (CDA)‐based P2P energy trading approach is used for enabling customers to trade energy with each other. To simulate human trader behaviour and maximises the benefits of customers, the authors considered the optimum bidding strategy through the zero intelligent plus trader model. The simulations executed on a 33‐bus power distribution grid and a 33‐node gas network indicate that the proposed framework can dramatically reduce the total operational cost and improve the performance of both networks. Using only the MINLP optimisation problem, first stage, the total operational cost of both networks is reduced by 15.58%, while the voltage profile at the end of the power grid is improved by about 7%. In the next stage, the total operating cost of both networks is further decreased by 29.31% via implementing the P2P energy trading mechanism.
... Each prosumer tries to maximize their profit, so the maximum revenue of the whole system cannot be guaranteed. Therefore, the market efficiency of decentralized peer-centric trading is low [32]. The trading form of a decentralized market is signing bilateral contracts, which means the energy transactions between prosumers and consumers are not visible to system operators. ...
With the high penetration of renewable energy resources on the demand side, peer-to-peer (P2P) energy sharing has emerged as a promising method for consuming the surplus energy produced by energy prosumers. This paper reviews 180 papers and summarizes the recent development of P2P energy trading environment, including the market structure, market mechanism, trading platforms, social sciences. We also discuss the relevant policy based on the review of the state-of-the-art literature, pilot projects and industrial practice. The cooperative and non-cooperative game theory-based models are specifically classified. The optimization algorithms in distributed and centralized structures are studied thoroughly. Deep reinforcement learning and blockchain-based optimization are two vital research directions to simulate the optimization-seeking process in the P2P energy sharing market. The constraints of the system network are also reviewed to consider the physical constraints during energy trading. We present the methodology discussion to classify and compare these optimization methodologies. In this paper, the relevant resource-sharing markets are modeled as extended P2P energy sharing markets. Emission right sharing, negawatt sharing, and energy storage are all carefully reviewed. The coupling of the relevant resource sharing and conventional energy sharing is discussed carefully. We also provide the conclusions containing the prospects and possible questions of P2P energy sharing at the end of this article. It can be concluded from the review result that P2P energy sharing is a promising research direction of the transactive energy market from the perspectives of both academia and industry.
... In centralized P2P power trading, single controller is used for an overall power network composed of communication links. The energy allocation algorithm is applied to a centralized P2P power trading market to achieve market equilibrium, and it maximizes global welfare [19]. In [20], the authors proposed a P2P energy financial space to encourage prosumers to establish merged power plants. ...
In order to replace fossil fuels with the use of renewable energy resources, unbalanced resource production of intermittent wind and photovoltaic (PV) power is a critical issue for peer-to-peer (P2P) power trading. To resolve this problem, a reinforcement learning (RL) technique is introduced in this paper. For RL, graph convolutional network (GCN) and bi-directional long short-term memory (Bi-LSTM) network are jointly applied to P2P power trading between nanogrid clusters based on cooperative game theory. The flexible and reliable DC nanogrid is suitable to integrate renewable energy for distribution system. Each local nanogrid cluster takes the position of prosumer, focusing on power production and consumption simultaneously. For the power management of nanogrid clusters, multi-objective optimization is applied to each local nanogrid cluster with the Internet of Things (IoT) technology. Charging/discharging of electric vehicle (EV) is performed considering the intermittent characteristics of wind and PV power production. RL algorithms, such as deep Q-learning network (DQN), deep recurrent Q-learning network (DRQN), Bi-DRQN, proximal policy optimization (PPO), GCN-DQN, GCN-DRQN, GCN-Bi-DRQN, and GCN-PPO, are used for simulations. Consequently, the cooperative P2P power trading system maximizes the profit utilizing the time of use (ToU) tariff-based electricity cost and system marginal price (SMP), and minimizes the amount of grid power consumption. Power management of nanogrid clusters with P2P power trading is simulated on the distribution test feeder in real-time and proposed GCN-PPO technique reduces the electricity cost of nanogrid clusters by 36.7%.
... The values are slightly increased and compared with commercial products due to the different scale of application in Ref. [78]. Appendix B. Time-of-use tariff structure Similar to Ref. [79] this work uses a ToU tariff that consists of three times: off-peak, shoulder and peak. While the FIT stays static over the whole period, the grid tariff varies according to the times. ...
To reduce CO2 emissions, the European Commission aims at having 100 Positive Energy Districts (PEDs) planned, developed or established by 2025. A PED annually exports more energy than it imports from the local grid. Because of Europe's diversity, this study aims to indicate where in the EU and under which tariff circumstances an electrified PED will likely thrive most. To do so, the work uses a tailor-made mixed-integer linear programming model to optimise electrified PED solutions and compare them to the respective status quo for various representative zone-tariff parameter combinations. Results indicate that the optimal potential for PEDs is in southern Europe, with a dynamic electricity tariff and where previously no district heating was used. Under those circumstances, the PED concept could save around 84% of carbon emissions while being more economical over the project horizon. Pricing of CO2 emissions of energy services additionally nudges towards PED implementation. By limiting the power exchange of the PED with the grid, some of the negative grid impacts can be reduced. This study provides an essential insight into where in Europe a PED could be a sensible addition compared to where other decarbonisation approaches might be more beneficial.
... In addition, different trading mechanisms will affect the trading results of the energy market. The P2P trading mechanisms include various types, such as High-Low matching [32], centralized market clearing [33], and continuous double auction (CDA) [34]. Those mechanisms have differences in the trading process and results. ...
In the deregulated energy market environment, small-scale peer-to-peer (P2P) energy trading can increase the distributed photovoltaic power generation consumption and promote local energy balance. However, it also increases the possibility of security constraints violations during the operation of the utility grid. To solve physical network congestion caused by distributed P2P energy trading, we propose a method that considers the time sequence of P2P energy trading based on a continuous double auction (CDA) mechanism. In addition, to make full use of the users’ flexibility to solve the network congestion, a two-tier market (P2P energy market and ancillary service market) coordination mechanism is proposed. The congestion management model includes two parts. One is congestion responsibility determination and congestion cost allocation in the P2P energy market, and the other is flexible services purchasing in the ancillary service market for minimizing congestion costs. The cost and benefit constraints are specially added in the congestion management model to reduce the economic loss of the grid and users. The proposed method is tested on 11-bus and IEEE 33-bus radial distribution systems to illustrate the effectiveness and scalability of this method. The simulation results show that the proposed mechanism and model effectively perform congestion management considering distributed P2P energy trading, and the results are profitable to users participating in both markets.
