Smart PV-Storage-Charger System Boosts Profit by 28% with New Energy Management Strategy
A groundbreaking energy management strategy for integrated photovoltaic, battery energy storage, and electric vehicle charging systems has demonstrated a remarkable 28% increase in daily revenue under real-world operating conditions. Developed by a research team from Zhejiang Baima Lake Laboratory and its affiliated institutions, the innovation centers on a DC-bus architecture that enhances efficiency, reduces grid dependency, and optimizes economic returns through intelligent use of time-of-use electricity pricing.
As the global transition to electric mobility accelerates, the infrastructure required to support widespread EV adoption faces mounting challenges. Despite rapid growth in EV ownership, charging infrastructure development has lagged, creating a critical imbalance between vehicle demand and charging availability. In China, the vehicle-to-charger ratio stood at 2.7:1 in 2021, far from the targeted 1:1 benchmark set in earlier national development plans. This gap highlights a systemic issue: conventional charging stations are not only constrained by grid capacity, especially in older urban areas, but also struggle with profitability due to reliance on a single revenue stream—charging service fees.
To address these challenges, researchers have increasingly turned to integrated solutions that combine solar generation, battery storage, and EV charging into a unified microgrid system. Known as “PV-storage-charging” or “PV-BES-Charger” systems, these setups offer a self-sustaining energy ecosystem capable of reducing peak grid demand, lowering operational costs, and improving energy resilience. However, the effectiveness of such systems hinges on the sophistication of their energy management strategies.
Traditional approaches to energy optimization in these systems often rely on advanced algorithms such as genetic algorithms or particle swarm optimization. While effective in simulation environments, these methods require accurate forecasting of both solar generation and charging demand—two variables that are inherently unpredictable in real-world settings. Moreover, they demand significant computational power, making them impractical for deployment on cost-effective, embedded controllers like microcontrollers commonly used in commercial charging hardware.
In response, Guodong He, Changyong Fang, Ling Hong, Rongmin Wu, Peng Hou, and Ding Wu from Zhejiang Baima Lake Laboratory, Zhejiang Energy Group R&D, and the Key Laboratory of Solar Energy Utilization & Energy Saving Technology in Zhejiang Province have introduced a novel, real-time energy management strategy designed specifically for low-resource embedded systems. Their work, published in Energy Engineering, presents a practical, scalable solution that maximizes economic returns without requiring complex predictive models or high-performance computing.
The core of the innovation lies in the system’s DC-bus electrical architecture, a departure from the more common AC-bus configuration used in conventional integrated systems. In traditional setups, energy from solar panels must first be converted from DC to AC via inverters before being fed into the grid or used to charge batteries and EVs. Each conversion step incurs efficiency losses, typically ranging from 3% to 8% per stage. By contrast, the proposed system operates on a DC bus, allowing photovoltaic arrays, battery storage, and EV chargers to interface directly through DC-DC converters, minimizing energy conversion steps and preserving system efficiency.
In this architecture, the battery energy storage system (BESS) is directly connected to the DC bus, where it plays a dual role: stabilizing the bus voltage and serving as an energy buffer that enables seamless power exchange between the grid, solar panels, and electric vehicles. This design allows the system to dynamically route power along the most efficient pathways, avoiding unnecessary conversions and reducing overall energy losses.
One of the key insights from the research is the identification of optimal power flow paths based on conversion efficiency. The team mapped out ten possible energy flow routes within the system and calculated their respective end-to-end efficiencies. Paths involving fewer conversion stages—such as direct grid-to-vehicle or solar-to-vehicle—naturally exhibited higher efficiencies, while routes passing through both the battery and multiple converters suffered greater losses. For instance, delivering power directly from the grid to the EV charger achieved 95.2% efficiency, whereas routing power from the grid to the battery and then to the vehicle dropped efficiency to 88.7%.
Armed with this understanding, the researchers formulated a real-time decision-making framework that prioritizes the most efficient energy pathways while aligning with economic objectives. The primary goal is to minimize net energy cost, defined as the difference between electricity purchased from the grid and revenue generated from charging services, factoring in time-of-use (TOU) tariffs. In regions like Zhejiang, where electricity prices vary significantly between peak, shoulder, and off-peak hours, strategic use of storage can yield substantial savings.
The strategy operates on a simple yet powerful principle: maximize self-consumption of solar energy, charge the battery during low-cost off-peak periods, and discharge during high-price peak or critical peak periods. This approach not only reduces reliance on expensive grid power during peak demand but also helps flatten load curves, contributing to grid stability.
Crucially, the strategy is designed to be computationally lightweight. Instead of solving complex optimization problems in real time, it relies on predefined rules and real-time measurements of solar output and vehicle charging demand. The only active control variable is the charging or discharging power of the battery, which is adjusted based on the current TOU period and the state of charge (SoC) of the battery. This rule-based logic can be easily implemented on a microcontroller, making it ideal for mass deployment in commercial charging stations.
To validate the strategy, the team conducted a case study using a smart charging station installed in a commercial office park in East China. The system featured a 20 kW photovoltaic array, a 51.2 kWh lithium iron phosphate (LFP) battery, and a 60 kW DC fast charger capable of serving multiple vehicles throughout the day. The LFP chemistry was selected for its excellent cycle life, thermal stability, and safety profile—critical attributes for a system expected to undergo frequent charge-discharge cycles in a commercial setting.
The simulation assumed a clear-sky day with typical solar irradiance patterns in the region, resulting in a total daily PV generation of 135 kWh. Three charging events were modeled to reflect non-commute usage patterns: a 30-minute session at 9:00 AM, a one-hour session at 12:30 PM, and another 30-minute session at 7:30 PM—times when commercial users might charge during lunch breaks or after work. Each session drew power at the maximum rate of 60 kW, totaling 120 kWh of energy delivered to vehicles.
Under a baseline scenario without battery optimization—where the system simply used solar power when available and drew from the grid otherwise—the net revenue was calculated based on a service fee of 0.5 yuan per kWh on top of the TOU electricity cost. In this case, the total income from charging services was 161.6 yuan, with 60 yuan representing pure profit after accounting for electricity costs.
When the proposed energy management strategy was applied, the battery followed a “two-charge, two-discharge” pattern aligned with Zhejiang’s TOU tariff structure. During the overnight off-peak period (10 PM to 8 AM), the battery was charged slowly at 4.1 kW to avoid thermal stress and extend lifespan. In the midday off-peak window (11 AM to 1 PM), it absorbed excess solar energy and grid power to replenish 40% of its capacity. Then, during the afternoon and evening peak periods, the battery discharged strategically: first at 6.83 kW from 8–11 AM, and then at a higher rate of 20.48 kW during the critical peak window from 7–9 PM.
This optimized dispatch allowed the system to avoid purchasing expensive grid electricity during peak hours. Even when the EV charger demanded 60 kW, the grid supply never exceeded 50 kW, thanks to the combined contribution of solar and battery power. The result was a dramatic improvement in economics: the total daily revenue increased to 204.5 yuan, representing a 28% gain over the baseline scenario.
Beyond financial performance, the strategy demonstrated several operational advantages. The battery operated within a 10% to 90% SoC window, avoiding deep cycling that accelerates degradation. Charge and discharge rates were kept low—peaking at 0.4C (approximately 20.5 kW for a 51.2 kWh battery)—further enhancing longevity. The system also reduced peak grid demand, which could translate into lower demand charges for commercial operators and reduced strain on local distribution networks.
The implications of this research extend beyond individual charging stations. As cities and utilities seek to integrate more distributed energy resources, solutions like this offer a blueprint for decentralized, resilient, and economically viable infrastructure. Unlike centralized grid upgrades, which are costly and time-consuming, smart microgrid-enabled chargers can be deployed incrementally, scaling with demand.
Moreover, the strategy’s compatibility with low-cost hardware makes it accessible to a wide range of stakeholders, from small businesses to municipal operators. By eliminating the need for cloud-based optimization or machine learning models, the system remains functional even in areas with limited connectivity, ensuring reliability and uptime.
The success of this approach also underscores a broader shift in energy management philosophy—from predictive, model-heavy systems to adaptive, rule-based logic that responds to real-time conditions. In a world where uncertainty is the norm, simplicity and robustness often outperform complexity.
As EV adoption continues to rise, the integration of renewable generation and storage will become not just an option, but a necessity. Systems that can intelligently balance supply and demand, reduce costs, and enhance sustainability will play a pivotal role in shaping the future of transportation.
The work of He, Fang, Hong, Wu, Hou, and Wu offers a compelling example of how engineering ingenuity, grounded in practical constraints, can deliver tangible benefits. Their energy management strategy proves that even without advanced AI or big data analytics, significant improvements in efficiency and profitability are achievable—paving the way for smarter, more sustainable charging infrastructure across China and beyond.
Guodong He, Changyong Fang, Ling Hong, Rongmin Wu, Peng Hou, Ding Wu, Zhejiang Baima Lake Laboratory, Zhejiang Energy Group R&D, Key Laboratory of Solar Energy Utilization & Energy Saving Technology, Energy Engineering, DOI: 10.16189/j.nygc.2024.01.013