Among individuals and organizations, “sharing economy” refers to a collaborative consumption model where resources, services, or products are shared among participants instead of being them outright, which can increase the utilization of idle resources and optimize resource allocation (Kalathil et al., 2017; Mi and Coffman, 2019). The fundamental concept behind the sharing economy is to optimize resource allocation by increasing the utilization of idle resources in society. Prominent example of sharing economy platforms include Airbnb for room rental, Uber for ridesharing and car rental, and WeWork for office space sharing. These sharing economic behaviors can offer benefits such as cost savings, reduced environmental impact, and enhanced access to goods and services (Palliyaarachchi and Kariyawasam, 2021). Moreover, sharing economy also promotes a sense of community and collaboration among individuals, as it encourages people to connect and share experiences with one another (Rojanakit et al., 2022).

In response to the intensifying environmental challenges, conventional power structures are undergoing a transition towards low-carbon alternatives, characterized by a significant integration of renewable energy sources (Liu et al., 2024). For example, with the development of photovoltaic (PV) technology, PV panels have become increasingly popular for residential applications, often installed on rooftops. However, the integration of a large number of distributed PV systems into the grid pose technical challenges due to the intermittency and volatility of PV generation, impacting the safe operation of the grid (Wang et al., 2020).

Energy storage (ES), particularly battery storage, presents opportunities to address temporary power surpluses and shortages by decoupling energy generation from consumption (Walker and Kwon, 2021). Increasingly, households are incorporating ES devices to optimize renewable energy utilization, and reduce daily electricity costs. Nevertheless, the value of ES is often underestimated due to a narrow focus on price arbitrage between off-peak and on-peak hours (Liu et al., 2019). Within a certain geographical area, combining different forms of renewable energy sources, such as PV, wind power generation and ES, et al., can create a regional integrated energy system. Scholars have conducted research on this topic from multiple perspectives, such as economic dispatch in a distributed fashion (Huang et al., 2019), bilateral integrated demand response (Li et al., 2022), and load forecasting based on data-driven techniques (Zhu et al., 2022).

Given the rapid development of the increasing installation of distributed PV generation and ES device, combined with “sharing economy” concept, it becomes essential to explore effective methods for sharing ES capacity within the community. Existing research on ES capacity sharing, exemplified by (Xiao et al., 2022; Gul et al., 2022; Zhao et al., 2019), centers around the construction of public large-scale ES devices that enable users within a specific area to share. In contrast, This paper focuses on sharing idle ES capacity between residential users (RUs) and community shared equipment controllers (CSECs), aligning with the trend towards smaller capacity and decentralized layout of ES devices. The scheme discussed in this paper distinguishes itself from existing approaches in terms of the considered system model (Barbour et al., 2018), chosen methodology (Chakraborty et al., 2018; Huang et al., 2021), and analysis (Rahbar et al., 2016).

Many studies have focused on optimizing the operation of ES devices. For instance, Ref (Roberts et al., 2019). presented and validated the self-sufficiency of PV generation combined with battery storage in five Australian apartment buildings. Ref (Khani et al., 2016). explored the cooperation between ES and consumers to achieve energy arbitrage in the electricity market. Similar research can be found in Ref. (Zheng et al., 2014). Moreover, Ref (Aazami et al., 2022). demonstrated that coupling distributed energy generation sources like wind power plants couple with ES devices can mitigate the active power volatility of wind turbine. However, these studies primarily focus on the economic value of ES devices or the enhancement of system performance from an independent perspective, without considering shared ES.

Ref (Xiao et al., 2022). proposed an ES sharing framework that considers both storage capacity and power capacity to enhance energy efficiency and reduce electricity costs. Ref (Rahbar et al., 2016). introduced a central controller to coordinate energy trades between users and shared ES, along with an algorithm for continuous iteration. In Ref. (Wang et al., 2018), the concept of a “charging envelop” was introduced to dynamically allocate ES capacity between customers and the distribution network operator. Ref (Chang et al., 2022). proposed a multi-agent resource allocation algorithm to generate distributed strategies for sharing ES resources. While, the aforementioned studies configure shared ES capacity from an optimization or allocation perspective, they neglect the market mechanisms associated with shared ES.

Ref (Zhang et al., 2022). established a P2P market model for shared ES units and proposed a method for service pricing and load dispatching. Ref (He et al., 2011). conducted auctions for the right to utilize ES unit, ensuring non-conflicting usage by actors in different auctions. Ref (Taylor, 2014; Thomas et al., 2020). proposed mechanisms for financial ES rights and physical ES rights to promote the marketization of shared ES. In Ref. (Zaidi et al., 2018), a combinatorial auction mechanism was introduced, and a winner determination solution was proposed for ES capacity sharing.

These studies, among others, analyze ES capacity sharing from various perspectives, including optimal operation (Roberts et al., 2019; Aazami et al., 2022; Sechilariu et al., 2012), ES capacity allocation (Xiao et al., 2022; Wang et al., 2018; Chang et al., 2022), market mechanism (Zhang et al., 2022; Zaidi et al., 2018; Mediwaththe et al., 2019). However, they all assume that all RUs share a public large capacity ES unit, which can be funded by a third party like the social capital (Zhu et al., 2021) or co-funded by users in need (Chakraborty et al., 2018). Implementing a public shared ES unit jointly funded by users poses specific challenges. 1) When new users join or old users leave, how should the investment funds be settled? 2) User demand for ES is random and difficult to predict. How can the construction and maintenance cost be allocated fairly and reasonably? To our knowledge, no research has addressed these aspects. In addition, more and more users are choose to install individual small-capacity ES unit to meet their specific needs. Therefore, considering distributed individual ES units, we can develop new ideas to avoid the challenges mentioned above.

In this paper, we present a centralized sharing platform to facilitate individual ES capacity sharing between RUs and CSECs within residential community. We establish a local ES capacity sharing market, with the Resource Management Organization (RMO) acting as market administrator or auctioneer. Considering that market participants are inclined to maximize their own profits by leveraging their private information, we propose a specific market bidding process. In this process, competitive sellers (buyers) can iteratively adjust their bids using market information provided by the RMO until no further changes are made. The RMO determines the market price and ES capacity allocation using the proposed iterative uniform-price bidding mechanism (IUPBM). In the end, we conduct a case study that includes simulations. The results indicate that sharing ES capacity through the proposed mechanism is not only feasible but also beneficial for all market participants as a whole. In comparison to existing literature, this paper offers the following contributions:

The remainder of this paper is organized as follows: In Section 2, we formulate the system model and categorize the market types. Section 3 discusses the limitations of conventional uniform-price auctions and presents our proposed bidding mechanism. Section 4 introduces noncooperative competition among market participants and outlines the solution algorithm. Section 5 presents the case study and analyzes the results. Finally, Section 6 concludes the paper, discussing future work and implications.

Community shared equipment controllers (CSECs), such as elevators, water pumps, and corridor lights, are significant energy consumers that require a relatively large ES capacity for their operations (Tushar et al., 2014). However, limited space available in public area, along with concerns regarding maintenance, large investment costs, heat generation, and noise issues, restrict the use of ES devices. In this paper, we propose a solution where CSECs can share a fraction of the ES capacity owned by RUs through a third party, such as the RMO, in a local ES capacity sharing market, as shown in Figure 1. Each RU is assumed to have an energy management system (EMS) installed to record relevant energy data and optimize energy consumption. By sharing idle ES capacity, RUs can collectively share the investment cost, mitigate risks, and exploit economies of scale. Additionally, CSECs can benefit from reduced energy purchase costs through the shared ES capacity. This arrangement creates a mutually beneficial solution for both RUs and CSECs.

Each RU in the community is considered as an individual entity with their own ES device. We assume that there are N RUs, where N is cardinality of the set N = 1,2 , … , N representing the set of RUs. The total ES capacity owned by RU i ∈ N , doted as C i cap , represents the maximum amount of energy that can be stored. At time slot t, u _i,t represents the amount of ES capacity that RU i has utilized, and b _i,t represents the maximum remaining capacity that RU i can share with CSECs. The amount of ES capacity q _i,t that RU i plans to share with CSECs at time slot t is subject to the constraints 0 ≤ q i , t ≤ b i , t = C i cap − u i , t . The ES sharing capacity profile of RU i during the operation time is noted as q i = q i , 1 , q i , 2 , … , q i , T , which T is the total number of time slots during the operation time period, and T = 1,2 , … , T represents the set of all time slots with equal intervals Δt. Each RU has a different daily capital cost based on its charging/discharging behaviors. The cost of ES usage for RU i at time slot t, denoted as E i , t cost , can be expressed as E i , t cost = γ P i , t c h + P i , t dch t . (1) where γ represents the levelized cost of energy (LCOE) of the ES, P i , t c h and P i , t dch denote the charging and discharging power of the ES device owned by RU i at time slot t. It is worth noting that, from a technical perspective, simultaneous charge and discharge of the ES device is feasible. The operation constraints and the SOC of the ES devices are modeled as follows: u i , t = u i , t − 1 + P i , t c h η i , c h − P i , t dch / η i , d c h t . (2) 0 ≤ u i , t ≤ C i cap . (3) P i c h , m i n ≤ P i , t c h ≤ P i c h , m a x . (4) P i dch,min ≤ P i , t dch ≤ P i dch,max . (5) where P i c h , m i n , P i c h , m a x , and P i dch,min , P i dch,max represent the minimum and maximum charging and discharging power of the ES device owned by RU i.

In addition, there are M CSECs, where M is the cardinality of the set M = 1,2 , ⋯ M representing the CSECs. Each CSEC is assumed to have its own PV generation but requires additional ES capacities to store their excess energy. This is due to the fact that some CSECs either lack their own ES devices or their ES capacities are insufficient to store the energy they generate. CSEC j ∈ M has a storage requirement d _j,t at time slot t, and the storage requirement profile of CSEC j can be expressed as d j = d j , 1 , d j , 2 , … , d j , T .

The RMO acts as an administrator for the residential community, functioning as an auctioneer. Both CSECs and RUs submit their bids/offers to the RMO. Once all the necessary information is collected, the RMO performs the market clearing process based on specified market rules, as depicted in Figure 2.

Both RUs and CSECs have PV generation capabilities. The PV generation output for RU i at time slot t can be denoted as Q i , t P V , while for CSEC j, it can be represented as Q j , t P V . For RUs, they have the flexibility to choose how to utilize their PV generation. They can either store the energy in their ES or sell to the grid. The decision can be based on time-varying FiT price, denoted as p _FiT,t , which represents the price at which the grid is willing to purchase the excess energy. RUs can also opt for a combination of both storing the energy and selling it to the grid. On the other hand, CSECs prioritize using their PV generation to meet their own load requirements. Once their own load is satisfied, any surplus energy can be stored in the ES devices owned by RUs or sold to the grid, or a combination of both options. If the PV output combined with the previously stored energy is still insufficient to meet the energy demands of RUs or CSECs, they will need to purchase the remaining energy deficit from the grid.

In the residential community, the total ES sharing capacity quantity of RUs at time slot t can be represented as Q sup t = ∑ i = 1 N t q i , t where t ∈ T , N t ∈ N , and N _t denotes the number of RUs who are willing to share ES capacities at time slot t. Similarly, the total demand quantity of CSECs is Q dem t = ∑ j = 1 M t d j , t where t ∈ T , M t ∈ M , and M _t represents the number of CSECs who require ES capacity at the time slot t. After collecting all the bids/offers from CSECs and RUs, the RMO can calculate Q sup t and Q dem t . Based on the supply-demand relationship, the market can be categorized into the following situations:

Generally, in the uniform-price bidding mechanism (UPBM), bidders typically make only one bid. It is widely acknowledged that the UPBM can achieve social welfare optimality only if RUs provide their true offer information, and the market operates under perfect competition, where market participants act as price-takers and cannot anticipate the impact of their submitted information (Newbery, 1995; Kirschen and Strbac, 2018). However, achieving perfect competition in reality is extremely challenging. As a result, the seller’s side does not experience perfect competition. Moreover, all RUs are selfish and inclined to exploit their private information to maximize their profits. If RUs perceive an opportunity to make additional profit through strategic behavior, they are likely to pursue it without engaging in negotiations. This behavior often involves tactics such as withholding production, providing false offer prices, or a combination of both (Zhao et al., 2010). Each RU believes they have the potential to influence the market price and, therefore, may bid higher than their true offer price to secure extra profit. In other words, the existing UPBM lacks incentive compatibility. To illustrate this, we refer to a simple example of a two-bus network discussed in Ref (Tang and Jain, 2013). that highlights the strategic behavior of the winning seller with the highest offer price.

RU 1, RU2 and demand side are in line 1, RU three and RU4 are in line 2. The transmission capacity between lines one and two is denoted as Q, and the total demand is equals to 2Q. For simplicity, it is assumed that all RUs have constant marginal costs. The cost functions are represented as follows: C ₁ (x ₁) = 1.5αx ₁, C ₂ (x ₂) = 2αx ₂, C ₃ (x ₃) = αx ₃, C ₄ (x ₄) = αx ₄, where α > 0 is a parameter that can vary. If all sellers submit their true cost information, the economic dispatch is determined as ( x 1 * , x 2 * , x 3 * , x 4 * ) = ( Q , 0,0.5 Q , 0.5 Q ) . It is important to note that the economic dispatch is not unique, any allocation schedule where x 3 * + x 4 * = Q constitutes an optimal dispatch. RU one becomes the last winner, and the market price is set at 1.5α. The total cost is 2.5αQ, resulting in an overall profit of 0.5αQ. However, if RU one knows the cost information of RU 2 ∼ 4, they can strategically provide a false marginal cost β, where β ∈ (1.5α, 2α), specifically β = 1.9α. As a result, the economic dispatch remains the same as in the truth-telling case, but the market price increases to 1.9α. Consequently, the overall profit becomes 1.3αQ and the profits of all winning sellers increase compared to the previous situation.

Indeed, in situations where demand is inelastic and competition primarily occurs on the seller’s side, the market price can be artificially influenced and driven higher. This can potentially lead to tacit collusion among the sellers without any direct payment transfers between them. Theoretically, if the RUs engage in cooperative behavior, they can collectively increase their offer prices by the any same amount, represented as p → p + Δ, where Δ > 0. By raising the market price through this behavior, all RUs can benefit from the increased offer price, while maintaining the relative interest structure among themselves.

In this paper, we propose an iterative uniform-price bidding mechanism (IUPBM) for short-term (hour-ahead) ES capacity sharing, aiming to provide economic benefits to both RUs and CSECs.

To ensure the successful and reasonable implementation of the proposed mechanism, we outline several important requirements as follows:

The trading process of the IUPBM is depicted in Figure 3 and comprises three main stages: market judgment, market bidding, and market settlement. During the market judgment phase, the RMO assesses whether the market operates in a buyer’s market or a seller’s market based on the supply and demand conditions. Subsequently, the proposed IUPBM is implemented accordingly. In the settlement phase, the physical delivery of ES sharing capacity and the market price are determined based on the market clearing results. The buyer pays the corresponding fee to the RMO, who then compensates the seller accordingly.

Taking all the aforementioned requirements into consideration, we present a comprehensive outline of the trading process for the proposed IUPBM mechanism within a single cycle:

The market clearing rule in the IUPBM comprises two key components: the ES capacity pricing rule and the allocation rule. The IUPBM ensures the optimal allocation of limited market demand and idle ES capacity resources to the winning participants, considering their willingness-to-share or willingness-to-pay. The mathematical representations of the market clearing process in both market types are presented below:

We choose the publicly available Pecan Street data set from Austin, Texas, United States (Pecan Street), for the reason that this region has a high potential for PV production, and each house has a PV generation system and a smart meter that provides high time granularity data. We select data from 12 out of 25 residential users in 18 May 2018, for the numerical case analysis. Specifically, we use eight groups of data for RUs and four groups for CSECs. The PV generation profiles are shown in Figure 4.

Given the historical dataset, we assume that RUs are equipped with an ES unit. The installed ES capacity for each RU is randomly determined and listed in Table 1. The maximum charging and discharging rates for ES units are set proportional to the installed capacity as P i , t c h , max = P i , t dch,max = C i cap / 16 K W h (Tesla powerwall technical specs, 2023). The charging and discharging efficiencies are set at η _i,ch = η _i,dch = 90%. The levelized cost of energy (LCOE) for the ES γ is set at 9.5 cent/KWh. Additionally, we consider a 1-day planning horizon divided into 24 1-h time periods for ES capacity sharing. The initial ES level at the beginning is randomly set to reflect the differences between RUs.

Unless otherwise specified, the default settings mentioned above will be used for the numerical experiments. We will compare two different trading mechanisms: (i) the IUPBM mechanism in this paper and (ii) the traditional uniform price bidding mechanism (UPBM). The performance measures for the experiments include, but are not limited to: (i) ES capacity sharing price, (ii) utilities of RUs and CSECs, and (iii) RUs’ ES capacity usage. All the numerical experiments will be implemented using MATLAB R2023a with Gurobi Slover.