In the evolving landscape of modern electricity markets, the participation of demand-side resources has become increasingly critical. As a researcher focused on energy systems, I have observed that integrated energy buildings, which combine various distributed energy resources, are emerging as key players in these markets. However, individual buildings often face limitations in flexibility and capacity when participating directly. To address this, aggregation through third-party agents or aggregators can pool resources from multiple buildings, enabling more effective market engagement. A particularly promising resource in this context is the battery electric vehicle (BEV), which not only serves as a transportation tool but also as a mobile energy storage unit. Its ability to move between locations and time-shift energy offers unique opportunities for optimizing energy flows within building clusters. This article explores a day-ahead bidding strategy for integrated energy building clusters that leverages electric energy sharing and the mobile storage characteristics of battery electric vehicles. I will present a comprehensive model, including operational mechanisms, cluster modeling of BEVs, building resource integration, and a bidding optimization framework, supported by formulas and tables to summarize key aspects. The goal is to demonstrate how this approach can reduce operational costs and enhance market participation for building clusters.
The integration of renewable energy sources and the development of interconnected power grids have accelerated the transformation of electricity markets. Demand-side resources, such as integrated energy buildings, are now actively participating in markets like the day-ahead market to provide flexibility and balance supply and demand. These buildings typically incorporate photovoltaic (PV) systems, energy storage systems, micro gas turbines, and absorption chillers, allowing for combined cooling, heating, and power generation. However, their individual capacity is often insufficient for significant market impact. By forming clusters and enabling energy sharing among buildings through distribution networks, aggregators can coordinate these resources to bid collectively in the day-ahead market. The addition of battery electric vehicles as mobile storage units adds a dynamic layer to this coordination. BEVs can charge during low-price periods, discharge during high-price periods, and physically transport energy between buildings based on travel patterns. This mobility transforms them from static storage into a distributed network of energy assets, which I refer to as the “mobile storage characteristic.” In this work, I propose a strategy that considers both electric energy sharing and BEV mobility to optimize day-ahead bidding for building clusters.
To implement this strategy, I first establish the operational mechanism and framework for the integrated energy building cluster participating in the day-ahead market. The cluster consists of multiple buildings, each with its own energy management system (EMS). A third-party agent, acting as an aggregator, coordinates energy sharing among buildings and formulates the day-ahead bidding strategy for the entire cluster. Each building reports predicted energy consumption, production data, and BEV travel information to the agent. The agent then optimizes power distribution, considering market prices and constraints, to minimize total operational costs. The framework involves two key components: modeling BEV clusters based on travel chains and modeling building resources. For BEVs, I use travel chain analysis to group vehicles according to their movement patterns between residential, commercial, and office areas. This allows for predicting the spatiotemporal distribution of BEVs and their energy storage availability. The building resource model includes PV generation, battery storage, micro gas turbines, and absorption chillers, with constraints on operational limits and energy balances.
The BEV cluster model is central to leveraging mobile storage. I categorize BEVs into groups based on travel chains, such as simple chains (e.g., home-work-home) and complex chains (e.g., home-work-commercial-home). The departure time from a region \(C\) (e.g., home \(H\), work \(W\), or commercial \(S\)) follows a probability distribution:
$$F(T_c) = \frac{1}{\sigma_c \sqrt{2\pi}} \exp\left(-\frac{(T_c – \mu_c)^2}{2\sigma_c^2}\right)$$
where \(T_c\) is the first departure time, \(\mu_c\) is the mean, and \(\sigma_c\) is the standard deviation. The spatial movement is described by a state transition matrix \(P_{ij}(t)\) for each time period \(t\) (with 1-hour intervals), representing the probability of moving from region \(i\) to region \(j\). For example, the matrix for regions \(H, S, W\) is:
$$P_{ij}(t) = \begin{bmatrix} P_{11}(t) & P_{12}(t) & P_{13}(t) \\ P_{21}(t) & P_{22}(t) & P_{23}(t) \\ P_{31}(t) & P_{32}(t) & P_{33}(t) \end{bmatrix}, \quad (1=H, 2=S, 3=W)$$
This model predicts the number of BEVs in each building at any time, enabling the agent to schedule charging and discharging. For each BEV group \(k\) in building \(b\) at time \(t\), the charging and discharging power are optimized. Let \(N_{b,k,t}\) be the number of BEVs in group \(k\), \(P_{b,k,t}^{\text{EV,ch}}\) and \(P_{b,k,t}^{\text{EV,dis}}\) be the total charging and discharging power of the group, and \(P_{b,t}^{\text{EV,ch}}\) and \(P_{b,t}^{\text{EV,dis}}\) be the average per-BEV power. The constraints include:
$$\delta_{b,k,t}^{\text{ch}} + \delta_{b,k,t}^{\text{dis}} \leq 1$$
$$P_{b,t}^{\text{EV,ch}} = \frac{P_{b,k,t}^{\text{EV,ch}}}{N_{b,k,t}}, \quad P_{b,t}^{\text{EV,dis}} = \frac{P_{b,k,t}^{\text{EV,dis}}}{N_{b,k,t}}$$
$$0 \leq P_{b,t}^{\text{EV,ch}} \leq \delta_{b,k,t}^{\text{ch}} P_{b,t}^{\text{EV,max}}, \quad 0 \leq P_{b,t}^{\text{EV,dis}} \leq \delta_{b,k,t}^{\text{dis}} P_{b,t}^{\text{EV,min}}$$
The state of charge (SOC) of the BEV battery is updated as:
$$S_{\text{soc},b,t}^{\text{EV}} = S_{\text{soc},b,t-\Delta t}^{\text{EV}} + \left( P_{b,t}^{\text{EV,ch}} \eta_{\text{ch}}^{\text{EV}} – \frac{P_{b,t}^{\text{EV,dis}}}{\eta_{\text{dis}}^{\text{EV}}} \right) \Delta t$$
with bounds \(S_{\text{soc}}^{\text{EV,min}} \leq S_{\text{soc},b,t}^{\text{EV}} \leq S_{\text{soc}}^{\text{EV,max}}\), where \(\eta_{\text{ch}}^{\text{EV}}\) and \(\eta_{\text{dis}}^{\text{EV}}\) are charging and discharging efficiencies, and \(\Delta t\) is the time interval. This formulation captures the flexibility of battery electric vehicles as mobile storage units.
For building resources, I model key components. The micro gas turbine (MT) generates electricity and waste heat. Its power output \(P_{b,t}^{\text{MT}}\) is:
$$P_{b,t}^{\text{MT}} = F_{\text{gas}} F_{b,t}^{\text{MT}} \eta^{\text{MT}}$$
where \(F_{\text{gas}}\) is the calorific value of natural gas, \(F_{b,t}^{\text{MT}}\) is the gas input, and \(\eta^{\text{MT}}\) is the efficiency. The output is limited by \(0 \leq P_{b,t}^{\text{MT}} \leq P_{b,t}^{\text{MT,max}}\). The absorption chiller (AC) uses waste heat from the MT for cooling, with cooling power \(Q_{b,t}^{\text{AC}}\) given by:
$$Q_{b,t}^{\text{AC}} = \eta^{\text{HE}} \times \gamma^{\text{MT}} \times P_{b,t}^{\text{MT}} \times \delta^{\text{AC}}$$
where \(\eta^{\text{HE}}\) is heat exchanger efficiency, \(\gamma^{\text{MT}}\) is the MT’s heat-to-power ratio, and \(\delta^{\text{AC}}\) is the AC’s coefficient of performance. The battery storage system in the building has similar constraints to BEVs but is stationary. Let \(P_{b,t}^{\text{BT,ch}}\) and \(P_{b,t}^{\text{BT,dis}}\) be charging and discharging powers, with efficiencies \(\eta_{\text{ch}}^{\text{BT}}\) and \(\eta_{\text{dis}}^{\text{BT}}\). The energy content \(W_{b,t}^{\text{BT}}\) evolves as:
$$W_{b,t}^{\text{BT}} = W_{b,t-\Delta t}^{\text{BT}} + \left( P_{b,t}^{\text{BT,ch}} \eta_{\text{ch}}^{\text{BT}} – \frac{P_{b,t}^{\text{BT,dis}}}{\eta_{\text{dis}}^{\text{BT}}} \right) \Delta t$$
with \(W_{b,1}^{\text{BT}} = W_{b,24}^{\text{BT}}\) for daily cycle consistency. The PV system output \(P_{b,t}^{\text{PV}}\) is:
$$P_{b,t}^{\text{PV}} = \eta^{\text{PV}} S_{b}^{\text{PV}} I_t$$
where \(\eta^{\text{PV}}\) is conversion efficiency, \(S_{b}^{\text{PV}}\) is panel area, and \(I_t\) is solar irradiance.
The day-ahead bidding model aims to minimize the total operational cost \(C\) of the building cluster, which includes equipment running costs, maintenance costs for PV and batteries, BEV scheduling costs, and revenue from the day-ahead energy market. The objective function is:
$$\min C = C^{\text{S}} + C^{\text{PV}} + C^{\text{BT}} + C^{\text{EV}} – C^{\text{en}}$$
where each component is defined as follows. The equipment running cost \(C^{\text{S}}\) sums over all buildings \(b\) and time periods \(t\):
$$C^{\text{S}} = \sum_{b=1}^{N} \sum_{t=1}^{24} \left( P_{b,t}^{\text{MT}} C_{b}^{\text{MT}} + F_{b,t}^{\text{MT}} C_{b}^{\text{F}} \right)$$
with \(C_{b}^{\text{MT}}\) as MT maintenance cost and \(C_{b}^{\text{F}}\) as gas purchase cost. The PV maintenance cost \(C^{\text{PV}}\) is:
$$C^{\text{PV}} = \sum_{b=1}^{N} \sum_{t=1}^{24} F_{b,t}^{\text{PV}} C_{b}^{\text{PV}}$$
where \(C_{b}^{\text{PV}}\) is the PV maintenance cost per unit. The battery maintenance cost \(C^{\text{BT}}\) is:
$$C^{\text{BT}} = \sum_{b=1}^{N} \sum_{t=1}^{24} \left| P_{b,t}^{\text{BT,ch}} + P_{b,t}^{\text{BT,dis}} \right| C_{b}^{\text{BT}}$$
with \(C_{b}^{\text{BT}}\) as the cost coefficient. The BEV scheduling cost \(C^{\text{EV}}\) accounts for the wear and tear on battery electric vehicles:
$$C^{\text{EV}} = \sum_{b=1}^{N} \sum_{k=1}^{K} \sum_{t=1}^{24} \left( P_{b,k,t}^{\text{EV,ch}} + P_{b,k,t}^{\text{EV,dis}} \right) C_{b}^{\text{EV}}$$
where \(C_{b}^{\text{EV}}\) is the cost per unit power for BEV operation. The energy market revenue \(C^{\text{en}}\) is based on predicted market prices for selling \(m_t^{\text{pn}}\) and buying \(m_t^{\text{en}}\):
$$C^{\text{en}} = \sum_{b=1}^{N} \sum_{t=1}^{24} \left( m_t^{\text{pn}} P_{b,t}^{\text{pn}} – m_t^{\text{en}} P_{b,t}^{\text{en}} \right)$$
where \(P_{b,t}^{\text{pn}}\) is the power sold to the market (as a generator) and \(P_{b,t}^{\text{en}}\) is the power purchased from the market (as a consumer). The constraints include energy balances for electricity and cooling. The electrical balance for each building \(b\) at time \(t\) is:
$$P_{b,t}^{\text{im}} + P_{b,t}^{\text{PV}} + P_{b,t}^{\text{BT,dis}} + P_{b,t}^{\text{MT}} + \sum_{k} P_{b,k,t}^{\text{EV,dis}} + P_{b,t}^{\text{en}} = P_{b,t}^{\text{el}} + P_{b,t}^{\text{ex}} + P_{b,t}^{\text{BT,ch}} + \sum_{k} P_{b,k,t}^{\text{EV,ch}} + P_{b,t}^{\text{pn}}$$
Here, \(P_{b,t}^{\text{im}}\) is power imported from other buildings, \(P_{b,t}^{\text{ex}}\) is power exported to other buildings, and \(P_{b,t}^{\text{el}}\) is the electrical load. For cooling, the balance is \(Q_{b,t}^{\text{AC}} = Q_{b,t}^{\text{cl}}\), where \(Q_{b,t}^{\text{cl}}\) is the cooling load. Indoor temperature constraints ensure comfort: \(T_{b,t}^{\text{in,min}} < T_{b,t}^{\text{in}} < T_{b,t}^{\text{in,max}}\).
To illustrate the model’s application, I conducted a case study with three types of integrated energy buildings: residential (Building 1), commercial (Building 2), and office (Building 3). Each building has PV, battery storage, a micro gas turbine, and an absorption chiller. Building 1 initially hosts 60 battery electric vehicles, while Buildings 2 and 3 have 20 each. BEV travel patterns are predicted using travel chain models, with parameters derived from typical data. The day-ahead market prices for buying and selling are as shown in Table 1, which summarizes key input data for the case study.
| Parameter | Building 1 (Residential) | Building 2 (Commercial) | Building 3 (Office) | Notes |
|---|---|---|---|---|
| PV capacity | 50 kW | 80 kW | 60 kW | Based on roof area and efficiency |
| Battery storage | 100 kWh | 150 kWh | 120 kWh | With 90% charge/discharge efficiency |
| Micro gas turbine | 30 kW max | 50 kW max | 40 kW max | Efficiency ηMT = 0.35 |
| BEV initial count | 60 vehicles | 20 vehicles | 20 vehicles | Battery capacity 50 kWh per BEV |
| BEV travel chains | H-W-H dominant | Mixed H-S-W | H-W-S-H complex | Using probability distributions |
| Market prices | Time-varying: high during 8-20h, low otherwise | From historical day-ahead data | ||
The optimization model is solved using a mixed-integer linear programming solver, and results are analyzed for different scenarios to validate the proposed strategy. I compare four scenarios: Scenario 1 (no energy sharing, no BEV mobility), Scenario 2 (no energy sharing, with BEV mobility), Scenario 3 (energy sharing, no BEV mobility), and Scenario 4 (energy sharing with BEV mobility, i.e., the proposed strategy). Additionally, I consider variations in parameters like line capacity and BEV energy consumption to test robustness. The key output is the day-ahead bidding strategy for the building cluster, represented by the total power to buy or sell in each time period. Table 2 summarizes the operational costs for each scenario, highlighting the impact of energy sharing and BEV mobility.
| Scenario | Equipment Running Cost | Market Participation Cost | Total Operational Cost | Cost Reduction vs. Scenario 1 |
|---|---|---|---|---|
| Scenario 1 | 1422.5 | 5987.2 | 7409.7 | 0% |
| Scenario 2 | 1171.0 | 5621.3 | 6792.3 | 8.34% |
| Scenario 3 | 1100.0 | 5514.6 | 6614.6 | 10.73% |
| Scenario 4 | 1058.3 | 5345.2 | 6403.5 | 13.58% |
The results show that Scenario 4, which combines energy sharing and BEV mobility, achieves the lowest total operational cost, with a reduction of 13.58% compared to Scenario 1. This demonstrates the synergy between these two mechanisms. Specifically, the mobile storage characteristic of battery electric vehicles allows for better utilization of excess PV generation during low-price periods. For instance, BEVs can charge in buildings with surplus solar power and then discharge in buildings with high demand during peak price hours, effectively shifting energy across time and space. Moreover, energy sharing enables buildings to trade power internally, reducing reliance on the external market. The bidding strategy in Scenario 4 involves less power purchase during high-price periods and more power sale, as shown in Figure 1 (conceptual representation). This aligns with the goal of minimizing costs by arbitraging price differences.
To delve deeper, I analyze the BEV charging and discharging schedules. In Scenario 4, the BEV clusters exhibit smooth power profiles, with charging concentrated during low-price periods (e.g., overnight) and discharging during high-price periods (e.g., midday). This not only reduces grid stress but also extends BEV battery life by avoiding rapid cycles. The mobility aspect is crucial: BEVs moving from residential to office areas transport stored energy, enabling buildings to share resources without physical wires. For example, a battery electric vehicle charged at home in the morning can discharge at the office during peak hours, providing power and reducing the office’s market purchase. This dynamic is captured in the BEV cluster model through the state transition matrices. The flexibility of battery electric vehicles as mobile storage units enhances the overall resilience of the building cluster.
Furthermore, I examine the impact of parameter variations. In Scenario 5, with reduced line capacity for energy sharing, the total cost slightly increases but remains close to Scenario 4, indicating that BEV mobility can compensate for grid limitations. In Scenario 6, with increased building distances and higher BEV energy consumption, costs rise due to reduced efficiency, yet the proposed strategy still outperforms others. This underscores the robustness of the approach. The mathematical formulation ensures that all constraints are satisfied, including BEV SOC limits, building temperature ranges, and device operational bounds. The optimization problem can be expressed compactly as:
$$\min_{\mathbf{x}} \left\{ \mathbf{c}^T \mathbf{x} : \mathbf{Ax} \leq \mathbf{b}, \mathbf{x}_{\text{min}} \leq \mathbf{x} \leq \mathbf{x}_{\text{max}} \right\}$$
where \(\mathbf{x}\) includes variables like power flows, BEV charging/discharging, and market bids, \(\mathbf{c}\) is the cost vector, and \(\mathbf{A}\) and \(\mathbf{b}\) encode linear constraints.
In discussion, the integration of battery electric vehicles into building energy management offers significant benefits. First, it decarbonizes transportation and energy use by coupling with renewables. Second, it provides grid services through demand response. Third, the mobile storage characteristic enables spatial arbitrage, which static storage cannot achieve. However, challenges include predicting BEV travel patterns accurately and ensuring user participation. Future work could incorporate uncertainty in BEV movements using stochastic optimization or machine learning. Additionally, expanding the model to include vehicle-to-grid (V2G) capabilities could further enhance flexibility. The role of the aggregator is vital in coordinating these resources, and regulatory frameworks must support such aggregated participation in electricity markets.
In conclusion, this article presents a day-ahead bidding strategy for integrated energy building clusters that leverages electric energy sharing and the mobile storage characteristics of battery electric vehicles. I have developed a comprehensive model that includes BEV cluster modeling based on travel chains, building resource integration, and an optimization framework for market participation. The case study demonstrates that combining energy sharing with BEV mobility reduces total operational costs by 13.58%, primarily by decreasing power purchase during high-price periods and increasing power sales. The battery electric vehicle emerges as a key enabler of this strategy, providing both temporal and spatial flexibility. As electricity markets evolve, such approaches can enhance the economic and environmental performance of building clusters, contributing to a more resilient and sustainable energy system. Future research should explore real-world data integration, scalability to larger clusters, and inclusion of other distributed energy resources.