Abstract
The rapid advancement of technology has propelled global industry forward, leading to an exponential increase in the demand for electricity. The reliance on fossil fuels to meet this demand has resulted in significant energy crises and environmental pollution. Consequently, there has been a shift towards renewable energy sources (RES) such as wind and solar power. However, the intermittent and fluctuating nature of RES poses challenges to the stability of the power grid. Battery Energy Storage Systems (BESS) have emerged as a viable solution to mitigate these issues, ensuring the safe and stable operation of the grid. Given the high cost of energy storage batteries, it is crucial to optimize their placement and capacity. This paper focuses on reducing the average daily network loss in distribution networks by optimizing the capacity allocation of distributed Battery Energy Storage System in networks incorporating wind and solar power.
1. Introduction
1.1 Background and Significance
The increasing industrialization and urbanization have led to a surge in electricity demand. Fossil fuels, which are non-renewable and environmentally detrimental, have been the primary source of energy. The global energy crisis and environmental degradation have necessitated the exploration of clean, renewable energy sources. Wind and solar energy, being abundant and sustainable, have gained significant attention. However, their inherent variability and intermittency pose challenges to grid stability. Battery Energy Storage System, with its ability to store and release energy efficiently, offers a solution to these challenges.
1.2 Current Research Status
1.2.1 Major Energy Storage Technologies
Energy storage technologies have evolved to address the challenges posed by RES. These technologies can be broadly classified into physical, electromagnetic, electrochemical, and phase change storage. Each technology has its advantages and limitations, as summarized in Table 1.1.
| Energy Storage Technology | Advantages | Disadvantages |
|---|---|---|
| Pumped Hydro Storage | Large-scale storage, cost-effective, long lifespan | Geographical constraints, high water demand |
| Compressed Air Energy Storage | Scalable, no toxic materials required | Low efficiency, requires specific containers |
| Flywheel Energy Storage | Fast response, long lifespan, high efficiency | Low energy density, high cost |
| Superconducting Magnetic Energy Storage | High energy return rate, fast discharge | High equipment cost |
| Battery Energy Storage System | High energy density, efficient, versatile | High cost, limited lifespan, safety concerns |
| Supercapacitors | Fast response, long lifespan | Low energy density, high cost, rapid self-discharge |
1.2.2 Battery Energy Storage System Capacity Allocation and Optimization Methods
The optimization of Battery Energy Storage System capacity allocation is crucial for enhancing grid stability and efficiency. Various models and algorithms have been proposed to address this issue. Genetic algorithms (GA) and simulated annealing (SA) are commonly used due to their robustness and ability to handle complex optimization problems. However, these algorithms have limitations, such as slow convergence and susceptibility to local optima. This paper proposes an improved simulated annealing genetic algorithm (ISAGA) to overcome these limitations.
1.3 Main Work of This Paper
This paper aims to optimize the capacity allocation of Battery Energy Storage System in distribution networks incorporating wind and solar power. The main contributions are:
- Introduction to Battery Energy Storage System Components and Applications: Detailed explanation of Battery Energy Storage System components and their functions, along with various application scenarios.
- Mathematical Modeling: Development of mathematical models for wind and solar power output, as well as Battery Energy Storage System charging and discharging.
- Optimization Model: Establishment of an optimization model for Battery Energy Storage System capacity allocation with the objective of minimizing daily network loss.
- Algorithm Improvement: Proposal of an improved simulated annealing genetic algorithm (ISAGA) to enhance the optimization process.
- Simulation and Analysis: Conducting simulation experiments on the IEEE-33 test system to validate the proposed algorithm.
2. Distributed Battery Energy Storage System Composition and Main Application Scenarios
2.1 Composition of Distributed Battery Energy Storage System
A distributed Battery Energy Storage Systemconsists of several key components, including the battery system (BS), power conversion system (PCS), battery management system (BMS), and supervisory control and data acquisition (SCADA) system. Each component plays a vital role in ensuring the efficient operation of the Battery Energy Storage System.
2.1.1 Battery System Function and Composition
The BS is the core component responsible for storing and releasing energy. It is typically composed of multiple battery cells connected in series or parallel to form battery modules, clusters, and ultimately, the complete BS. The configuration of the BS can be adjusted based on the required capacity and application.
2.1.2 Power Conversion System Function and Classification
The PCS acts as the interface between the BS and the grid, controlling the flow of energy. It can convert DC power from the batteries to AC power for the grid and vice versa. The PCS can operate in four quadrants, enabling advanced applications such as black start, peak shaving, and frequency regulation.
2.1.3 Battery Management System Function
The BMS monitors and manages the state of the battery, ensuring safety and efficiency. It provides critical information such as state of charge (SOC), state of health (SOH), and temperature, enabling optimal operation and prolonging the battery’s lifespan.
2.2 Main Application Scenarios of Distributed Battery Energy Storage System
Battery Energy Storage System finds applications across various sectors, including user-side, grid-side, and generation-side.
2.2.1 User-Side Applications
- Peak Shaving and Valley Filling: Battery Energy Storage System can store energy during off-peak hours and release it during peak hours, reducing electricity costs and grid stress.
- Integrated Energy Systems: Battery Energy Storage System enhances the efficiency and reliability of integrated energy systems in industrial parks and residential areas.
- Demand Response: Battery Energy Storage System can participate in demand response programs, providing grid services and earning revenue.
2.2.2 Grid-Side Applications
- Grid Stability and Reliability: Battery Energy Storage System can provide backup power during grid failures, ensuring uninterrupted power supply.
- Frequency Regulation: Battery Energy Storage System can quickly respond to frequency deviations, maintaining grid stability.
- Deferral of Grid Upgrades: Battery Energy Storage System can alleviate the need for costly grid upgrades by managing peak loads and reducing congestion.
2.2.3 Generation-Side Applications
- Renewable Energy Integration: Battery Energy Storage System can smooth the output of renewable energy sources, reducing variability and enhancing grid integration.
- Ancillary Services: Battery Energy Storage System can provide ancillary services such as frequency regulation and voltage support, improving grid reliability.
- Clean Heating: Battery Energy Storage System can store excess renewable energy for use in heating applications, reducing reliance on fossil fuels.
2.3 Impact of Distributed Battery Energy Storage System on Distribution Network Losses
The integration of Battery Energy Storage System into the distribution network can significantly reduce network losses. By managing the flow of active and reactive power, Battery Energy Storage System can optimize grid operation and minimize losses. The active power management involves peak shaving and valley filling, while reactive power management focuses on voltage regulation.
3. Optimization Model for Distributed Battery Energy Storage System Capacity Allocation Considering Wind and Solar Power
3.1 Wind and Solar Power Output Models
3.1.1 Wind Power Output Model
The output power of a wind turbine is determined by the wind speed, which follows a Weibull distribution. The relationship between wind speed and power output is given by:
[ P_w = \begin{cases}
0 & v < v_i, v > v_o \
\frac{P_0}{v_0^3 – v_i^3} v^3 – \frac{P_0}{v_0^3 – v_i^3} v_i^3 & v_i \leq v \leq v_o \
P_0 & v_o < v \leq v_i
\end{cases} ]
where PwPw is the wind power output, vv is the wind speed, vivi is the cut-in speed, vovo is the rated speed, and vivi is the cut-out speed.
3.1.2 Solar Power Output Model
The output power of a photovoltaic (PV) system depends on the solar irradiance and the efficiency of the PV panels. The power output is given by:
Ppv=ηSRPpv=ηSR
where PpvPpv is the PV power output, ηη is the efficiency of the PV panels, SS is the area of the PV panels, and RR is the solar irradiance.
3.2 Battery Energy Storage System Output Power Model
The Battery Energy Storage System output power is modeled based on the state of charge (SOC) and the charging/discharging efficiency. The SOC is updated as follows:
[ SOC(t) = \begin{cases}
(1-\beta) SOC(t-1) + \frac{P_c \Delta t \eta_c}{C_e} & \text{charging} \
(1-\beta) SOC(t-1) – \frac{P_d \Delta t}{C_e \eta_d} & \text{discharging}
\end{cases} ]
where SOC(t)SOC(t) is the SOC at time tt, ββ is the self-discharge rate, PcPc and PdPd are the charging and discharging power, ηcηc and ηdηd are the charging and discharging efficiencies, and CeCe is the rated capacity of the Battery Energy Storage System.
3.3 Power Flow Calculation Model Considering Wind and Solar Power
The integration of wind and solar power into the distribution network affects the power flow. The equivalent load at each node is calculated by subtracting the wind and solar power output from the total load. The power flow is then calculated using the Newton-Raphson method, which is suitable for radial distribution networks.
3.4 Battery Energy Storage System Capacity Allocation Model
The objective of the Battery Energy Storage System capacity allocation model is to minimize the daily network loss. The objective function is given by:
f=minPL=(∑a=1AIa2ZaT)f=minPL=(a=1∑ATIa2Za)
where PLPL is the network loss, TT is the time period, AA is the total number of branches, IaIa is the current in branch aa, and ZaZa is the impedance of branch aa.
4. Optimization Method for Distributed Battery Energy Storage System Capacity Allocation
4.1 Genetic Algorithm (GA)
GA is a heuristic optimization algorithm inspired by natural selection. It involves the following steps:
- Initialization: Generate an initial population of solutions.
- Selection: Select the fittest individuals based on their fitness values.
- Crossover: Combine the selected individuals to produce offspring.
- Mutation: Introduce random changes to the offspring to maintain diversity.
- Termination: Repeat the process until a termination condition is met.
4.2 Simulated Annealing (SA)
SA is a probabilistic optimization algorithm that mimics the annealing process in metallurgy. It involves the following steps:
- Initialization: Start with an initial solution and set an initial temperature.
- Neighbor Selection: Generate a neighboring solution.
- Acceptance: Accept the new solution based on the Metropolis criterion.
- Cooling: Reduce the temperature and repeat the process until convergence.
4.3 Improved Simulated Annealing Genetic Algorithm (ISAGA)
The ISAGA combines the strengths of GA and SA to overcome their limitations. The key improvements are:
- Iterative Temperature Control: The cooling schedule is adjusted based on the population’s fitness, allowing for faster convergence.
- Adaptive Crossover and Mutation: The crossover and mutation probabilities are adjusted dynamically based on the population’s fitness, preventing premature convergence.
- Simulated Annealing Mutation: The mutation probability is further adjusted using the SA’s Metropolis criterion, enhancing the algorithm’s ability to escape local optima.
5. Simulation Experiments and Case Analysis
5.1 Simulation System and Parameters
The proposed algorithm is tested on the IEEE-33 test system, which consists of 33 nodes and 32 branches. Wind and solar power are integrated at specific nodes, and the Battery Energy Storage System capacity allocation is optimized using the ISAGA.
5.2 Simulation Results
The simulation results demonstrate the effectiveness of the ISAGA in optimizing Battery Energy Storage System capacity allocation. The daily network loss is significantly reduced, and the algorithm converges faster compared to traditional GA and SA.
5.3 Comparison with Other Algorithms
The ISAGA outperforms both GA and SA in terms of solution quality and convergence speed. The adaptive mechanisms in ISAGA allow for better exploration of the solution space, resulting in more optimal Battery Energy Storage System capacity allocation.
6. Conclusion and Future Work
6.1 Conclusion
This paper presents a comprehensive approach to optimizing the capacity allocation of distributed Battery Energy Storage System in distribution networks incorporating wind and solar power. The proposed ISAGA effectively addresses the limitations of traditional optimization algorithms, providing a robust and efficient solution for minimizing daily network loss.
6.2 Future Work
Future research could focus on:
- Enhanced Battery Models: Incorporating more detailed battery models that consider factors such as aging and temperature effects.
- Operational Strategies: Developing detailed operational strategies for Battery Energy Storage Systembased on real-time data and predictive analytics.
- Economic Considerations: Integrating economic factors into the optimization model to assess the cost-effectiveness of Battery Energy Storage SystemS deployment.
- Multi-Objective Optimization: Extending the optimization model to include multiple objectives, such as minimizing cost and maximizing reliability.