Enterprise AI Analysis
Research on New Energy System Configuration Based on Multi-objective Optimization
Lei Ren, Naval University of Engineering, Wuhan, Hubei, China
Chuanyan Cao*, Naval University of Engineering, Wuhan, Hubei, China
In the context of energy structure transformation as well as "carbon peaking and carbon neutrality" strategy, this paper introduces non-dominated sorting genetic algorithm (NSGA-II) with elitist strategy to address the issues related to new energy system configuration, thereby constructing a dual-objective optimization model with the core of minimizing the total life cycle cost and maximizing the comprehensive performance of the system. Subsequently, this research further validates the effectiveness of the proposed model through case analysis. Relevant results reveal that the algorithm is capable of generating a Pareto optimal solution set with uniform distribution to provide diversified configuration schemes for decision makers, thus realizing economic and technical collaborative optimization. Simply put, this study not only enriches the theoretical scheme of new energy system configuration but also furnishes a solid scientific basis for related engineering applications.
Executive Impact
This research provides a powerful multi-objective optimization framework for new energy system configuration, delivering quantifiable economic and performance benefits crucial for strategic decision-making in the era of carbon neutrality.
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Pareto Optimal Solutions Identified
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Project Budget for System
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Minimum Capacity Requirement
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Max Performance Score Achieved
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The Multi-Objective Challenge
New energy system configuration is a complex multi-objective optimization problem, aiming to simultaneously optimize two or more conflicting functions. Decision-makers must balance initial investment and long-term operating costs against factors like energy conversion efficiency, reliability, and environmental benefits. Traditional single-objective methods often fail to capture these intricate trade-offs.
Defining Pareto Optimality
Unlike single-objective problems with a unique optimal solution, multi-objective problems yield a set of Pareto optimal solutions. These are candidate solutions where no objective can be improved without sacrificing at least one other objective. This set offers decision-makers diverse configuration schemes, allowing for optimal balance under different strategic orientations.
Non-dominated Sorting for Ranking
The NSGA-II algorithm begins by classifying solutions into non-dominated layers. A solution X₁ dominates X₂ if X₁ is better than or equal to X₂ on all objectives, and strictly better on at least one. The best non-dominated solutions form the first layer, which are then excluded to find the next layer, and so on, until all solutions are ranked.
Crowding Distance for Diversity
To ensure diversity among Pareto optimal solutions, NSGA-II employs crowding distance comparison. For solutions within the same non-dominated layer, those with a larger crowding distance (meaning they are in less 'crowded' areas of the solution space) are preferred. This mechanism helps generate a well-distributed set of solutions, offering a broader range of choices to decision-makers.
Minimizing Total Life Cycle Cost (F1)
The first primary objective (F1) is to minimize the total life cycle cost of the new energy system. This includes initial procurement (Pᵢxᵢ), installation (Cᵢxᵢ), and annual operating costs (C₀ᵢxᵢ) over the project's life span (T), considering the discount rate (r). The formula is given as: ∑(Pᵢxᵢ + (C₀ᵢ + Cᵢ) × (1 - (1+r)⁻ᵀ)/r).
Maximizing Comprehensive Performance (F2)
The second objective (F2) aims to maximize the system's comprehensive performance. This is a weighted sum of key technical indicators, including energy conversion efficiency (ηᵢ), mean time between failures (MTBFᵢ), and carbon emission reduction rate (CRᵢ). These are normalized against their maximum values across all devices to provide a comparative performance score.
Key Model Constraints & Assumptions
The optimization is subject to two main constraints: a budget constraint (∑Pᵢxᵢ ≤ B_max, where B_max is the maximum budget) and a capacity demand constraint (∑Capᵢxᵢ ≥ D_min, ensuring the minimum required energy storage capacity is met). To simplify complexity, the model operates under specific assumptions: Market Fixity (fixed device types, known parameters), Performance Independence (system performance is an aggregation of individual device performances), and Price Stability (fixed unit prices over the planning period).
Enterprise Process Flow: NSGA-II Operational Procedure
Initialization
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Offspring Generation
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Population Merging
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Non-dominated Sorting
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Computation of Crowding Degree
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Selection of New Parent Population
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Termination Condition
Project Background & Constraints: New Energy System
Under the macro background of vigorously promoting the transformation of energy structure, a new energy storage system is planned to be deployed in the survey region. Given the intermittent problem of renewable energy power generation in this region, the construction of this energy storage system aims to alleviate this problem and improve the stability of the energy supply. As a whole, the project budget is set at CNY 2 million. Meanwhile, the project clearly stipulates that the total energy storage capacity of the system shall not be less than 400 kWh. Taking into account the time value of funds, the discount rate of funds is set at 6.5%, with the design life of the project of 20 years.
The technical team evaluated the mainstream new energy storage system devices in the market. Diverse devices demonstrate differences in core technical indicators. The actual procurement and allocation process faces a major contradiction: despite the low initial investment and quick investment recovery, low-cost devices present short cycle life and low energy conversion efficiency, which leads to the potential increase in long-term operating costs. In contrast, high-performance devices involve a large upfront investment. Nonetheless, high-performance devices provide more stable and efficient energy services throughout their life cycle, thus significantly reducing the unit energy storage cost.
In summary, this project needs to employ the multi-objective optimization algorithm to identify the optimal balance between initial investment cost and comprehensive performance of devices under the dual conditions of budget constraint and capacity lower limit, aiming at selecting the device configuration with both economy and advancement in this region. To this end, this research selected 23 new energy system models from the market, with their original parameter data illustrated in Table 1.
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Lowest Total Cost Achieved (Solution 1)
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Highest Performance Score Achieved (Solution 6)
| Solution # | Total Cost (10,000 CNY) | Overall Performance Score | Budget Utilization Rate (%) | Total Capacity (kWh) | Main Device Configuration | Technical Characteristics | Applicable Scenarios |
|---|---|---|---|---|---|---|---|
| 1 | 156.8 | 6.82 | 78.4 | 402 | A1(3), G1(4), J1(3), A2(2) | Efficiency: 88.2%; Carbon Reduction: 56.3 t/year | Minimal financial pressure, high flexibility; basic technological performance. |
| 2 | 168.3 | 7.45 | 84.2 | 415 | A2(2), D1(3), G2(2), J1(2) | Efficiency: 89.5%; Carbon Reduction: 67.8 t/year | Low investment threshold, short payback; moderate scalability. |
| 3 | 178.9 | 8.12 | 89.5 | 428 | B1(2), D2(2), G2(2), J2(2), A3(1) | Efficiency: 91.5%; Carbon Reduction: 78.9 t/year | Balanced cost-performance; high technological maturity; medium-term payback. |
| 4 | 185.6 | 8.63 | 92.8 | 436 | B2(2), D3(2), G3(1), H1(1), J2(2) | Efficiency: 92.8%; Carbon Reduction: 86.4 t/year | Strong technical performance; significant operational efficiency gains; longer payback. |
| 5 | 192.4 | 9.25 | 96.2 | 445 | B3(1), E1(2), H1(1), I1(1), K1(1) | Efficiency: 94.2%; Carbon Reduction: 98.7 t/year | Advanced technology, high system reliability; high initial investment. |
| 6 | 198.7 | 9.81 | 99.4 | 452 | C2(2), I1(2), F1(1), H2(1) | Efficiency: 95.8%; Carbon Reduction: 112.5 t/year | Leading-edge technology, notable environmental benefits; high operational complexity. |
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Your AI Implementation Roadmap
Successfully integrate multi-objective optimization into your energy planning with our structured approach, ensuring a smooth transition and measurable results.
Phase 01: Discovery & Strategy Alignment
Comprehensive analysis of existing energy systems, strategic goals (e.g., carbon neutrality targets), budget constraints, and technical requirements. Define key objectives and performance indicators for optimization.
Phase 02: Data Collection & Model Customization
Gather detailed data on available new energy devices, including costs, efficiencies, lifespans, and emission reduction potential. Customize the multi-objective optimization model (like NSGA-II) to reflect specific project parameters and constraints.
Phase 03: Solution Generation & Pareto Analysis
Run the optimization model to generate a diverse set of Pareto optimal solutions, representing various trade-offs between cost and performance. Analyze the solutions to identify optimal configurations under different risk appetites and strategic priorities.
Phase 04: Implementation & Monitoring
Assist in the procurement and deployment of selected new energy system components. Establish monitoring frameworks to track actual performance against predicted outcomes, ensuring continuous optimization and adaptation to evolving market conditions.
Phase 05: Post-Implementation Review & Scaling
Conduct a thorough review of the system's performance and economic benefits. Leverage insights gained to scale successful configurations to other projects or integrate advanced AI for real-time operational optimization and predictive maintenance.
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