Bi-objective operation optimization of regional integrated energy system considering shared energy storage – Nature

Report on the Bi-Objective Operation Optimization of a Regional Integrated Energy System (RIES) with Shared Energy Storage

1.0 Introduction: Advancing Sustainable Development Goals through Energy Innovation

The global transition towards sustainable energy systems, as outlined in the United Nations’ Sustainable Development Goals (SDGs), faces significant challenges. A primary obstacle to achieving SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action) is the high capital investment required for energy storage, which is critical for stabilizing grids with high penetrations of renewable energy. This report details an optimization technology for Regional Integrated Energy Systems (RIES) that incorporates a shared energy storage model. This innovative approach, inspired by the sharing economy, aims to reduce operational costs, enhance energy utilization efficiency, and improve system flexibility. By creating a viable framework for shared energy infrastructure, this research contributes directly to SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities and Communities), providing a pathway for developing modern, resilient, and low-carbon power systems.

The study addresses a critical gap in existing research by integrating distributed energy storage resources under a central Energy Storage Aggregator (ESA). This model is designed to overcome the financial barriers of individual storage ownership, thereby accelerating the adoption of clean energy and supporting national decarbonization targets.

2.0 Framework for a Sustainable Regional Integrated Energy System

To promote responsible energy consumption and production in line with SDG 12, a fundamental RIES architecture incorporating shared energy storage was developed. This framework leverages the scale advantages of centralized management to optimize distributed energy resources.

• System Structure: The RIES is structured in three functional layers:
  1. Source Layer: Integrates renewable sources like photovoltaic (PV) systems and wind turbines (WT) with conventional power grids and natural gas networks, directly supporting SDG 7.
  2. Conversion Layer: Comprises energy conversion technologies such as heat pumps, electric boilers, and gas boilers to manage multi-energy vectors (electricity, heat, cooling).
  3. Storage Layer: Includes energy storage batteries, ice storage tanks, and heat storage tanks.
• The Role of the Energy Storage Aggregator (ESA): The ESA acts as a third-party agent that leases distributed energy storage capacity from multiple RIES participants. It aggregates these resources to provide grid services, optimize charge/discharge cycles across the entire region, and enhance storage utilization efficiency. This innovative business model builds sustainable infrastructure (SDG 9) without requiring massive individual investments.

3.0 Bi-Objective Optimization Model for Enhanced Sustainability

A dual-objective optimization model was formulated to minimize the operating costs for both the RIES and the ESA, thereby ensuring a mutually beneficial and economically sustainable ecosystem. This model explicitly incorporates environmental costs, aligning its objectives with SDG 13 (Climate Action).

3.1 RIES Optimization Objective

The primary goal for the RIES is to minimize total annual operating costs. This objective function is crucial for ensuring the “affordable” aspect of SDG 7. The costs considered include:

• Renewable energy operation and maintenance costs.
• Fuel consumption and ramping costs for conventional units.
• Fees paid to the ESA for shared energy storage services.
• Costs of purchasing electricity from the main grid.
• Carbon Emission Costs: A direct financial penalty for CO2 emissions from grid electricity and natural gas consumption, reinforcing commitment to SDG 13.
• Penalties for renewable energy curtailment, incentivizing maximum use of clean energy (SDG 7, SDG 12).

3.2 ESA Optimization Objective

The ESA’s objective is to minimize its net operating cost, ensuring its business model is viable and can support the broader energy transition. Its financial model is based on:

• Revenue from leasing storage capacity to RIES participants.
• Costs associated with energy losses during charge-discharge cycles.
• Costs of purchasing electricity from the grid for arbitrage opportunities.
• Rental costs paid to RIESs for the use of their storage assets.

3.3 System Constraints

The model operates under several constraints to ensure stable and reliable energy delivery, a key component of resilient infrastructure (SDG 9) and sustainable communities (SDG 11).

1. System Balance Constraints: Ensuring that the supply of electricity, heat, and cooling meets demand at all times.
2. Equipment Constraints: Respecting the operational limits and ramp rates of all energy generation and conversion equipment.
3. Network Constraints: Adhering to the supply capacity limits of the external power grid.

4.0 Solution Methodology: Chaos Sparrow Search Algorithm (COSSA)

To solve the complex, multi-variable optimization problem, a novel Chaos Sparrow Search Algorithm (COSSA) was developed. This algorithm enhances the traditional Sparrow Search Algorithm (SSA) by incorporating Tent chaos and Gaussian mutation. This methodological innovation (SDG 9) improves the algorithm’s ability to find the global optimal solution, preventing premature convergence and ensuring the RIES operates at maximum efficiency and minimal cost. The algorithm proceeds through the following steps:

1. Initialize the sparrow population using Tent chaotic mapping to ensure diverse coverage of the solution space.
2. Calculate the fitness of each solution based on the RIES and ESA objective functions.
3. Update the positions of sparrows (discoverers, joiners, and scouts) based on SSA rules.
4. Apply Gaussian mutation and Tent chaotic perturbation to avoid local optima and refine the search.
5. Terminate when the maximum number of iterations is reached and output the optimal operational strategy.

5.0 Case Study and Results: Validating the Contribution to SDGs

A case study was conducted using data from three neighboring RIESs in eastern China. Two operational modes were simulated: Mode 1 (independent operation with dedicated energy storage) and Mode 2 (operation with shared energy storage managed by an ESA).

5.1 Economic and Environmental Performance

The results demonstrate the significant benefits of the shared energy storage model in advancing sustainability goals.

• Contribution to SDG 7 (Affordable and Clean Energy): In Mode 2, the total operating costs for the three RIESs were reduced by 17.92%, 19.64%, and 19.78%, respectively, compared to Mode 1. This was achieved by reducing costly grid electricity purchases and optimizing the use of cheaper, locally generated renewable energy.
• Contribution to SDG 13 (Climate Action): The shared model led to substantial reductions in carbon emission costs by 27.77%, 30.51%, and 25.98% for the three RIESs. This is a direct result of decreased reliance on fossil fuel-based grid power.
• Contribution to SDG 12 (Responsible Consumption and Production): The ESA’s coordinated dispatch strategy significantly improved the utilization of energy storage assets and reduced the need for off-peak charging from the grid, promoting more efficient energy consumption patterns.

5.2 Infrastructure and Innovation Impact

• Contribution to SDG 9 (Industry, Innovation, and Infrastructure): The ESA model proved to be a profitable venture within the simulation, achieving a negative operating cost (profit) of $2.912536 million. This confirms the viability of the shared storage model as a sustainable business innovation that can attract investment and build resilient energy infrastructure.
• Contribution to SDG 11 (Sustainable Cities and Communities): The model demonstrates a practical pathway for communities to enhance their energy resilience and reduce their environmental footprint. By enabling greater integration of local renewable resources, it supports the development of self-sufficient and sustainable communities.

6.0 Conclusion and Policy Implications

This report confirms that a shared energy storage model, managed by an ESA, offers a powerful solution for optimizing the operation of Regional Integrated Energy Systems. The proposed framework significantly reduces operating costs, lowers carbon emissions, and enhances energy efficiency, thereby making direct and measurable contributions to multiple Sustainable Development Goals, including SDG 7, SDG 9, SDG 11, SDG 12, and SDG 13.

The findings suggest that policymakers should encourage the development of third-party energy service providers like ESAs through supportive regulatory frameworks and financial incentives. Such policies can unlock the value of distributed energy resources, accelerate the transition to clean energy, and help build the resilient, sustainable, and affordable energy infrastructure required for the future.

Analysis of Sustainable Development Goals in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

The article on optimizing regional integrated energy systems (RIES) with shared energy storage connects to several Sustainable Development Goals (SDGs). The primary goals addressed are:

• SDG 7: Affordable and Clean Energy: The core of the article focuses on improving energy efficiency, integrating renewable energy sources like solar (PV) and wind (WT), and reducing operational costs, which directly contributes to making energy more affordable and cleaner. The text states the technology is “conducive to… improving energy utilization efficiency.”
• SDG 9: Industry, Innovation, and Infrastructure: The paper introduces an innovative service model (“shared energy storage”), a new technological framework (RIES integrating an Energy Storage Aggregator), and an advanced optimization algorithm (COSSA). This aligns with building resilient infrastructure and fostering innovation, as it aims at “accelerating modernized power system development.”
• SDG 11: Sustainable Cities and Communities: The concept of a “regional integrated energy system” is designed for application at a community or city level. By optimizing energy use, reducing costs, and lowering emissions for users in industrial, commercial, and residential areas, the model contributes to creating more sustainable and efficient energy systems within communities.
• SDG 13: Climate Action: The article explicitly links its methodology to climate goals, stating it is “critically advancing the ’30·60′ dual-carbon goal.” It also includes “carbon emission cost” as a key variable in its optimization model, directly addressing the need to mitigate climate change by reducing emissions from energy consumption.

2. What specific targets under those SDGs can be identified based on the article’s content?

Based on the article’s discussion, the following specific SDG targets can be identified:

1. Target 7.2: Increase the share of renewable energy. The article’s framework integrates renewable energy sources like Photovoltaic (PV) systems and Wind Turbines (WT). The model aims to enhance “renewable energy output stability” and penalizes the abandonment of wind and solar power, thereby promoting their increased utilization in the energy mix.
2. Target 7.3: Double the global rate of improvement in energy efficiency. A primary objective of the proposed model is “improving energy utilization efficiency.” The shared energy storage model allows for better management of energy supply and demand, reducing waste and optimizing the performance of the entire system.
3. Target 9.4: Upgrade infrastructure and promote clean technologies. The article proposes a significant technological and infrastructural upgrade by integrating RIES with shared energy storage. This is presented as a novel, efficient, and environmentally sound technology that modernizes the power system by centralizing distributed energy resources to “enhance storage utilization efficiency.”
4. Target 13.2: Integrate climate change measures into policies and planning. The research directly supports national climate strategies, specifically mentioning “China’s energy strategy transformation” and the “’30·60′ dual-carbon goal.” By creating an operational model that quantifies and minimizes carbon emission costs, it integrates climate change considerations directly into energy system planning and operation.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

Yes, the article mentions and implies several quantitative indicators that can be used to measure progress:

• Reduction in Operating Costs: The article quantifies the economic efficiency of its model, stating that the shared energy storage mode leads to “17.92%, 19.64%, and 19.78% reductions in total operating costs for RIES1-3.” This serves as a direct indicator of improved energy efficiency and affordability (Target 7.3). The abstract also highlights a total cost reduction of “2.912536 million dollars.”
• Reduction in Carbon Emission Costs: This is a direct indicator for climate action (Target 13.2). The article provides specific figures, stating that “Carbon emission costs decrease by 27.77% (RIES1), 30.51% (RIES2), and 25.98% (RIES3) under M2 versus M1.” This is calculated based on carbon emission coefficients for grid electricity and natural gas.
• Increased Utilization of Renewable Energy: While not given as a single percentage, progress can be measured by the “Renewable energy abandonment penalty cost.” A lower penalty cost implies higher utilization of available renewable energy, contributing to Target 7.2. The model’s ability to enhance “renewable energy output stability” also points to this goal.
• Net Electricity Purchases from the Grid: The article shows that in the shared storage mode, “RIES1-3 exhibit 28–41% lower net electricity purchases than independent operations.” This indicates greater energy self-sufficiency and efficiency within the regional system, supporting Targets 7.3 and 9.4.

4. Summary Table of SDGs, Targets, and Indicators

Source: nature.com