Online distributed optimization for spatio-temporally constrained real-time peer-to-peer energy trading

This paper studies the real-time energy trading problem in a smart community consisting of a group of grid-connected prosumers with controllable loads, renewable generations and energy storage systems. We propose a peer-to-peer (P2P) energy trading system, which integrates energy trading with energy management, enabling each prosumer to jointly manage its energy consumption, storage scheduling and energy trading in a dynamic manner for smart communities consisting of a group of grid-connected prosumers with controllable loads, renewable generations and energy storage systems. The proposed community-based P2P energy trading system combines an online energy control and trading algorithm with a double auction mechanism. The energy control and trading algorithm is designed based on the Lyapunov theory, allowing each prosumer to independently determine its bid in each time slot only based on its current energy supply condition, while the trading price, which is determined via the double auction mechanism, reflects the collective energy supply conditions of all prosumers participating in energy trading. The integration of the Lyapunov-based energy control and trading algorithm and the double auction mechanism yields a dynamic energy trading pricing mechanism that induces the prosumers to participate in energy trading in a coordinated manner by influencing the energy consumption, energy charging/discharging and energy trading decisions of the prosumers. Numerical simulation results demonstrate that energy exchange in the proposed scalable energy trading system yields significant improvements in terms of energy cost savings and renewable energy utilization efficiency, while ensuring the fair sharing of the benefits reaped from energy trading among the prosumers.

In this article, an agent-based transactive energy (TE) trading platform to integrate energy storage systems (ESSs) into the microgrids’ energy management system is proposed. Using this platform, two different types of energy storage market models are proposed to promote local-level (within the microgrid) and communal- or global-level ESSs’ participation in the intra- and intermicrogrid TE markets. Also, a reinforcement learning algorithm known as simulated-annealing-based $Q$ -learning is used to develop bidding strategies for ESSs to participate in the TE markets. Besides energy trading, the proposed system also accounts for the losses caused by energy transactions between ESSs and microgrids using a complex current-tracing-based loss allocation method. The overall efficacy of the proposed energy market management system is demonstrated using a modified IEEE 123-bus distribution system with multiple microgrids and ESSs. Based on simulation results, it is observed that the proposed model can effectively reinforce the balance between the supply and the demand in the microgrids using the mix of local and global ESSs.

Flexibility assessment of a combined heat-power system (CHP) with energy storage under real-time energy price market framework

This paper describes the operation of the new realtime energy market at CAISO. The new real-time market utilizes the security constrained unit commitment (SCUC) and security constrained economic dispatch (SCED) programs to optimally determine resource commitments and dispatch schedules while observing resource and system real-time constraints [AI Cohen, et al., 1999]. The SCED calculates three prices: the dispatch interval ex-post market clearing price (MCP) and two weighted-average prices based on the dispatch interval ex-post MCP. This paper describes the scheduling activities of the CAISO new realtime market followed by the major scheduling applications. The scheduling market timeline and scheduling application interfaces are also described.

The current ancillary services markets in ISO New England include two components: a locational forward reserve market and a real-time co-optimized energy and reserve markets. The locational forward reserve market was initiated in 2003, and was a capacity-type Forward purchase of non-spinning reserves to address the lack of revenue experienced by existing non-spinning reserve resources with low capacity factors but with high availability. The real-time energy and reserve market was instituted in October of 2006 to procure 10-min spinning, 10-min non-spinning and 30-min operating reserves along with energy at a system-wide level as well as locationally on a 5-min basis. Different from many existing reserve market markets, this Real-time Reserve Market does not accept bids but has similar outcomes as the previous Real-time Markets. It uses Penalty prices (similar to price caps) to value reserves when the system is short of meeting requirements or re-dispatch costs (if any) to value reserves when supply meets or exceeds requirements. In addition, the real-time reserve market optimizes the level of reserve import to a local reserve zone, maximizing the utilization of transfer capacity over an interface. As of today, the ISO NE's ancillary service markets have been working successfully. And ISO NE's external and internal ISO Market Monitor assessments of the market seem to indicate that the market design is working.

This paper proposes the scheme of the price-based generation control (PbGC) in the real-time energy market. Compromising the global requirements of the independent system operator (ISO) and the individual profits of generation companies (GENCOs), we formulate the real-time energy market as a constrained optimal tracking problem. The quadratic objective function attempts to balance the power supply with the demand, and to stationarize the electricity price. The linear constraints present the price-responding dynamics of GENCOs. Numerical simulation results verify that the PbGC method is effective under various scenarios of demand patterns; furthermore it outperforms the existing multi-period optimal control (MpOC) approach on tracking errors and the price level.

Proliferated integration of variable renewable resources, especially wind power, in multi-area power systems brings about critical needs to coordinate balancing resources over the whole interconnected system. This paper proposes a coordination methodology in the real-time electricity market between regional interconnected power systems. The new method optimally dispatches generating units, determines interregional energy trading, and allocates regulation reserve throughout the entire system. The balancing resources in participating areas are shared and more effectively used to tackle the wind power uncertainty by means of a combined automatic generation control system. In order to comply with the existing operating philosophy of regional electricity markets, the augmented Lagrangian relaxation along with alternative direction method of multipliers are applied to provide a fully decentralized procedure with limited data interchanges between neighboring regions. Performance of the proposed method is examined on a two-area 6-bus and the modified three-area IEEE 118-bus test systems. The results confirm the applicability and effectiveness of the proposed model in providing flexible energy trading, regulation sharing, improved economic efficiency, and increased ability to integrate variable renewable energy into the power systems.

To further improve the economic benefits of operators and the low-carbon performance within the system, this paper proposes a hierarchical distributed low-carbon economic dispatch strategy for regional integrated energy systems (RIESs) based on the Alternating Direction Method of Multipliers (ADMM). First, the energy coupling relationships among conversion devices in RIESs are analyzed, and a structural model of RIES incorporating an energy generation operator (EGO) and multiple load aggregators (LAs) is established. Second, considering the stepwise carbon trading mechanism (SCTM) and the average thermal comfort of residents, economic optimization models for operators are developed. To ensure optimal energy trading strategies between conflicting stakeholders, the EGO and LAs are embedded into a master–slave game trading framework, and the existence of the game equilibrium solution is rigorously proven. Furthermore, considering the processing speed of the optimization problem by the operators and the operators’ data privacy requirement, the optimization problem is solved in a hierarchical distributed manner using ADMM. To ensure the convergence of the algorithm, the non-convex feasible domain of the subproblem bilinear term is transformed into a convex polyhedron defined by its convex envelope so that the problem can be solved by a convex optimization algorithm. Finally, an example analysis shows that the scheduling strategy proposed in this paper improves the economic efficiency of energy trading participants by 3% and 3.26%, respectively, and reduces the system carbon emissions by 10.5%.

With the technical growth and the reduction of deployment cost for distributed energy resources (DERs), such as solar photovoltaic (PV), energy trading has been recently encouraged to energy consumers, which can sell energy from their own energy storage system (ESS). Meanwhile, due to the unprecedented rise of greenhouse gas (GHG) emissions, some countries (e.g., Republic of Korea and India) have mandated using a renewable energy certificate (REC) in energy trading markets. In this paper, we propose an energy broker model to boost energy trading between the existing power grid and energy consumers. In particular, to maximize the profits of energy consumers and the energy provider, the proposed energy broker is in charge of deciding the optimal demand and dynamic price of energy in an REC-based energy trading market. In this solution, the smart agents (e.g., IoT intelligent devices) of consumers exchange energy trading associated information, including the amount of energy generation, price and REC. For deciding the optimal demand and dynamic pricing, we formulate convex optimization problems using dual decomposition. Through a numerical simulation analysis, we compare the performance of the proposed dynamic pricing strategy with the conventional pricing strategies. Results show that the proposed dynamic pricing and demand control strategies can encourage energy trading by allowing RECs trading of the conventional power grid.

Peer-to-peer (P2P) energy trading has emerged as a novel approach to enhancing the coordination and utilization of distributed energy resources (DERs) within modern power distribution networks. This study presents a techno-economic analysis of different DER characteristics, focusing on the integration of photovoltaic (PV) systems and energy storage systems (ESS) within a community-based P2P energy trading framework in Aswan, Egypt, under a time-of-use (ToU) electricity tariff. Eight distinct cases are evaluated to assess the impact of different DER characteristics on P2P energy trading performance and an unbalanced low-voltage (LV) distribution network by varying the PV capacity, ESS capacity, and ESS charging power. To the best of the authors’ knowledge, this is the first study to comprehensively examine the effects of different DER characteristics on P2P energy trading and the associated impacts on an unbalanced distribution network. The findings demonstrate that integrating PV and ESS can substantially reduce operational costs—by 37.19% to 68.22% across the analyzed cases—while enabling more effective energy exchanges among peers and with the distribution system operator (DSO). Moreover, DER integration reduced grid energy imports by 30.09% to 63.21% and improved self-sufficiency, with 30.10% to 63.21% of energy demand covered by community DERs. However, the analysis also reveals that specific DER characteristics—particularly those with low PV capacity (1.5 kWp) and high ESS charging rates (e.g., ESS 13.5 kWh with 2.5 kW inverter)—can significantly increase transformer and line loading, reaching up to 19.90% and 58.91%, respectively, in Case 2. These setups also lead to voltage quality issues, such as increased voltage unbalance factors (VUFs), peaking at 1.261%, and notable phase voltage deviations, with the minimum Vb dropping to 0.972 pu and maximum Vb reaching 1.083 pu. These findings highlight the importance of optimal DER sizing and characteristics to balance economic benefits with technical constraints in P2P energy trading frameworks.

With the development of renewable resource, the power generated by several renewable resources need to supply the load Simultaneously. Multi-input DC/DC converter is proposed for this application. In order to stabilize the power generation of renewable energy, energy storage system is widely used. Generally, the storage system is the intermediate links, which supply the power to load under the condition of low input voltage. A novel dual-input DC/DC converter is proposed in this paper. The power generation of renewable energy and battery can deliver the energy to load simultaneously or separately. The utilization efficiency of the system is improved. On the condition of no load, power generation of renewable energy can charge the battery. The proposed converter expands the applications of the dual-input buck converter. The battery can be used as an independent power source to improve the output voltage of the converter. And the PV source can charge the battery when the converter has light or no load. The operation principle of the proposed converter is analyzed in detail. The analysis of inductor current in different stages is given. The simulation results verify the feasibility of converter.

This paper investigates a fully decentralized model for electricity trading within a transactive energy market. The proposed model presents a peer-to-peer (P2P) trading framework between the clients. The model is incorporated for industrial, commercial, and residential energy hubs to serve their associated demands in a least-cost paradigm. The alternating direction method of multipliers (ADMM) is implemented to address the decentralized power flow in this study. The optimal operation of the energy hubs is modeled as a standard mixed-integer linear programming (MILP) optimization problem. The corresponding decision variables of the energy hubs operation are transferred to the peer-to-peer (P2P) market, and ADMM is applied to ensure the minimum data exchange and address the data privacy issue. Two different scenarios have been studied in this paper to show the effectiveness of the electricity trading model between peers, called integrated and coordinated operation modes. In the integration mode, there is no P2P energy trading while in the coordinated framework, the P2P transactive energy market is taken into account. The proposed model is simulated on the modified IEEE 33-bus distribution network. The obtained results confirm that the coordinated model can efficiently handle the P2P transactive energy trading for different energy hubs, addressing the minimum data exchange issue, and achieving the least-cost operation of the energy hubs in the system. The obtained results show that the total operating cost of the hubs in the coordinated model is lower than that of the integrated model by $590.319, i.e. 11.75 % saving in the costs. In this regard, the contributions of the industrial, commercial, and residential hubs in the total cost using the integrated model are $3441.895, $596.600, and $988.789, respectively. On the other hand, these energy hubs contribute to the total operating cost in the coordinated model by $2932.645, $590.155, and $914.165 respectively. The highest decrease relates to the industrial hub by 14.8 % while the smallest decrease relates to the residential hub by 1 %. Furthermore, the load demand in the integrated and coordinated models is mitigated by 13 % and 17 %, respectively. These results indicate that the presented framework could effectively and significantly reduce the total load demand which in turn leads to reducing the total cost and power losses.

Smart energy technologies, services, and business models are being developed to reduce energy consumption and emissions of CO2 and greenhouse gases and to build a sustainable environment. Renewable energy is being actively developed throughout the world, and many intelligent service models related to renewable energy are being proposed. One of the representative service models is the energy prosumer. Through energy trading, the demand for renewable energy and distributed power is efficiently managed, and insufficient energy is covered through energy transaction. Moreover, various incentives can be provided, such as reduced electricity bills. However, despite such a smart service, the energy prosumer model is difficult to expand into a practical business model for application in real life. This is because the production price of renewable energy is higher than that of the actual grid, and it is difficult to accurately set the selling price, restricting the formation of the actual market between sellers and consumers. To solve this problem, this paper proposes a small-scale energy transaction model between a seller and a buyer on a peer-to-peer (P2P) basis. This model employs a virtual prosumer management system that utilizes the existing grid and realizes the power system in real time without using an energy storage system (ESS). Thus, the profits of sellers and consumers of energy transactions are maximized with an improved return on investment (ROI), and an intelligent demand management system can be established.

Against the backdrop of high investment costs in distributed energy storage systems, this paper proposes a bi-level energy management model based on shared multi-type energy storage to enhance system economics and resource utilization efficiency. First, an electricity–heat–hydrogen coupled shared storage architecture is developed, incorporating hydrogen-blended gas turbines, gas boilers, and hydrogen loads to achieve deep coupling between the power grid and natural gas network. Then, a bi-level game model is formulated with the upper-level objective of minimizing the storage operator’s cost and the lower-level objective of minimizing the cost of the integrated energy microgrid (IEM) aggregator. A cooperative game mechanism is introduced within the microgrids to support peer-to-peer energy trading. Nash bargaining theory is applied to determine benefit allocation and dynamic pricing strategies among microgrids. The model is solved using a genetic algorithm (GA) and the alternating direction method of multipliers (ADMM). Simulation results validate the proposed strategy’s effectiveness and feasibility in reducing system costs, improving overall benefits, and achieving fair benefit allocation.

With the rapid popularization of photovoltaics (PVs), battery energy storage systems (BESSs) and electric vehicles (EVs) in residential microgrids, more and more prosumers have participated in actively trading energy with peers, which promotes the appearance of peer-to-peer (P2P) energy trading mode. Since this P2P trading could enhance the grid’s reliability and improve the social welfare in the system, trading mechanism research, in the context of P2P energy trading, have attracted considerable attentions. However, in recent studies, there still are some limitations, such as a lack of proper pricing strategies and a lack of consideration about forward energy trading. To solve these problems, this paper designs a P2P energy trading mechanism based on both hour-ahead market and real-time market. In the proposed mechanism, to increase P2P transactions and maximize social benefits at each market, some proper pricing strategies, such as a risk-implemented simultaneous game-theoretic approach and auction-based pricing strategy, have been adopted. Meanwhile, the proposed mechanism also considers a balance responsible party (BRP) to offer reserve services for realizing purposes. Moreover, the effectiveness of the proposed trading mechanism has been verified by several case studies.