Two-layer energy scheduling of electrical and thermal smart grids with energy hubs including renewable and storage units considering energy markets – Nature

 

Report on an Advanced Energy Management Framework for Sustainable Smart Grids

Executive Summary

This report details a two-layer energy management method for the operation of integrated electrical and thermal smart grids. The framework is designed to enhance the participation of renewable energy hubs in energy markets, directly contributing to several United Nations Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). By employing a two-stage, bi-level optimization approach, the system coordinates energy sources and storage to maximize profitability and operational efficiency. The methodology successfully models and manages uncertainties in renewable generation and load, achieving significant performance improvements. Simulation results indicate an 18% enhancement in economic performance and an 18-27% increase in operational efficiency, demonstrating a robust pathway toward more resilient, affordable, and clean energy systems.

1.0 Introduction: Aligning Energy Systems with Sustainable Development Goals

The global imperative to mitigate climate change (SDG 13) necessitates a fundamental transition from fossil fuels to Renewable Energy Sources (RES). This transition is central to achieving Affordable and Clean Energy (SDG 7) and building Sustainable Cities and Communities (SDG 11). A key strategy in this transition is the development of intelligent energy systems that can efficiently manage diverse energy carriers.

1.1 Motivation and Background

Modern distribution systems are increasingly complex, integrating technologies like combined heat and power (CHP) units and various energy storage systems (ESSs). To manage this complexity and advance sustainability, the concept of the Energy Hub (EH) has emerged. An EH is an integrated unit that can convert, store, and supply multiple energy types (e.g., electricity and heat), optimizing the use of both renewable and non-renewable sources. Effective management of these hubs is critical for several reasons:

• Enhanced Efficiency: Coordinated control of energy sources and loads within an EH improves overall system efficiency, supporting SDG 7’s target on energy efficiency.
• Increased Renewable Integration: EHs facilitate the seamless integration of intermittent RES like wind and bio-waste, advancing the SDG 7 target on increasing the share of renewable energy.
• Economic Viability: Optimal operation allows EHs to participate profitably in energy markets, creating a business case for clean energy infrastructure and contributing to economic aspects of SDG 9.
• Grid Stability and Resilience: By providing flexibility and managing local energy flows, EHs enhance the stability and resilience of the broader energy infrastructure, a cornerstone of SDG 9 and SDG 11.

1.2 Identified Challenges and Opportunities for Sustainable Energy Management

Previous research has highlighted several gaps that hinder the full realization of the SDGs through energy systems. This study identifies and addresses these challenges as opportunities for innovation:

1. Coordination and Data Management: Traditional, centralized control models are becoming inefficient as the number of distributed energy resources grows. A hierarchical, two-layer management system is needed to streamline communication and decision-making, making the grid smarter and more responsive (SDG 9).
2. Market Integration and Flexibility: The inherent uncertainty of RES creates a discrepancy between day-ahead (DA) planning and real-time (RT) operation. This “flexibility gap” must be managed to ensure grid stability and maximize the economic potential of clean energy. This requires advanced modeling of both DA and RT markets.
3. Diversification of Clean Technologies: To build a truly resilient and sustainable system (SDG 9, SDG 11), a diverse portfolio of clean technologies is essential. This includes leveraging innovative sources like CHP-based Bio-waste Units (BUs), which convert waste to energy, and advanced storage like hydrogen and compressed air, which offer long-duration storage capabilities crucial for supporting high RES penetration (SDG 7).
4. Advanced Optimization and Uncertainty Modeling: Solving the complex, non-linear problem of EH operation requires robust and efficient algorithms. Furthermore, accurately modeling uncertainties in load, price, and RES generation is critical for reliable and effective planning.

1.3 Report Objectives and Contributions to SDGs

This report presents a framework designed to address the aforementioned challenges. The primary contributions, which directly support the achievement of the SDGs, are:

• A Two-Stage Optimization Model: Models both DA and RT market participation to minimize flexibility costs, ensuring the reliable integration of renewables (SDG 7, SDG 13).
• A Two-Layer Energy Management System (EMS): Establishes a clear coordination structure between hub components and grid operators, improving operational efficiency and supporting the development of resilient infrastructure (SDG 9).
• Integration of Advanced Clean Technologies: Incorporates CHP-based BUs, hydrogen storage, and compressed air storage to enhance system flexibility and promote a circular economy (SDG 7, SDG 11, SDG 12).
• A Hybrid Evolutionary Algorithm: Utilizes a novel combination of Honey-Bee Mating Optimization (HBMO) and Artificial Bee Colony (ABC) algorithms for a reliable, fast, and accurate solution.
• Stochastic Uncertainty Modeling: Employs the Unscented Transformation (UT) method to efficiently and accurately model uncertainties, leading to more robust and realistic operational planning.

2.0 Proposed Energy Management Framework

The proposed framework is structured as a two-stage optimization process, each stage containing a bi-level problem that reflects the interactions between different stakeholders in the energy system. This hierarchical approach is designed to promote efficiency and align with the goals of sustainable infrastructure (SDG 9).

2.1 Stage 1: Day-Ahead (DA) Hourly Operation

The first stage focuses on the hourly scheduling for the next day, aiming for economic efficiency, a key component of Affordable Energy (SDG 7).

• Upper Level (Grid Operator Perspective): The objective is to minimize the cost of energy purchased from the main grid. This is subject to the physical constraints of the electrical and thermal networks, such as power flow limits and voltage levels, ensuring the grid operates safely and reliably (SDG 9, SDG 11).
• Lower Level (Energy Hub Operator Perspective): The objective is to maximize the profit of each EH by participating in the DA electricity and heat markets. This considers the operational constraints of all internal components, including RES generation and the charging/discharging of storage systems. This incentivizes the efficient use of clean energy assets.

2.2 Stage 2: Real-Time (RT) 5-Minute Scheduling

The second stage adjusts the DA plan in near-real-time to account for unforeseen fluctuations in renewable generation and load, which is critical for grid stability.

• Upper Level (Grid Operator Perspective): The objective shifts from cost minimization to minimizing the flexibility cost. This cost represents the imbalance between the DA plan and RT reality. Minimizing it ensures the system remains balanced and resilient, which is vital for integrating high levels of intermittent renewables (SDG 7, SDG 13).
• Lower Level (Energy Hub Operator Perspective): The EH operator again seeks to maximize profit, but now within the 5-minute RT market, using its flexible assets (especially storage) to respond to grid needs and correct imbalances.

2.3 Modeling and Solution Methodology

To solve this complex, multi-objective problem, a sophisticated methodology was employed:

1. Uncertainty Modeling: The Unscented Transformation (UT) technique was used to model uncertainties in market prices, load demand, and RES output. This parametric method is computationally efficient and provides the necessary inputs to assess system flexibility accurately.
2. Problem Simplification: The bi-level optimization problem was converted into a single-level problem using the Karush–Kuhn–Tucker (KKT) method. This mathematical reformulation makes the problem computationally tractable without losing critical constraints.
3. Hybrid Optimization Solver: A hybrid algorithm combining the Artificial Bee Colony (ABC) and Honey-Bee Mating Optimization (HBMO) methods was developed. This approach leverages the strengths of both algorithms to find a reliable and optimal solution faster than conventional methods, demonstrating innovation in computational techniques for sustainable systems (SDG 9).

3.0 Results and Discussion

The framework was tested on a system comprising a 9-bus electrical network and a 7-node thermal grid with seven interconnected energy hubs. The results validate the effectiveness of the proposed approach in advancing key sustainability metrics.

3.1 Performance of the Hybrid Solver

The proposed hybrid HBMO+ABC algorithm demonstrated superior performance compared to other standard algorithms. It achieved the optimal solution with the fastest convergence time (113.2 seconds) and the lowest standard deviation (0.95%), indicating a highly reliable and unique solution. This computational efficiency is crucial for real-world application in RT energy markets.

3.2 Impact of the Two-Layer Coordinated Model

The coordinated, two-layer EMS was compared against an uncoordinated model and a single-layer model. The results clearly show the benefits of coordination:

• Increased Profitability: The coordinated model increased EH profits by over 18% in the DA market and 12% in the RT market compared to the uncoordinated approach. This economic incentive is vital for the deployment of clean energy technologies (SDG 7).
• Reduced Imbalance: The flexibility cost (imbalance between DA and RT) was more than halved in the coordinated model, showcasing its ability to manage RES intermittency effectively.
• Computational Efficiency: The two-layer model was 62% faster to solve than a single-layer model where the grid operator manages everything centrally, proving its suitability for complex, large-scale systems.

3.3 Contribution to Network Performance and the SDGs

The implementation of the proposed framework yields significant improvements in the technical and economic performance of the energy networks, directly contributing to SDG targets.

• Economic Improvement (SDG 7): The total operating cost for the energy networks was reduced by 18.3% compared to a baseline case without optimized EHs. This demonstrates a direct contribution to more affordable energy.
• Enhanced Efficiency (SDG 7, SDG 12): Expected energy losses across the networks were reduced by 20%. This improvement in operational efficiency promotes responsible energy consumption and production.
• Improved Infrastructure Resilience (SDG 9, SDG 11): The system saw a 27% reduction in maximum voltage drop and an 18% reduction in maximum temperature drop. This signifies a more stable and resilient grid, capable of providing reliable power to communities and industries, even with high RES penetration.

4.0 Conclusion and Future Outlook

The two-layer, two-stage energy management framework presented in this report offers a robust and effective solution for integrating renewable energy hubs into smart electrical and thermal grids. By coordinating the operation of diverse clean energy assets and actively participating in energy markets, the system achieves significant economic and technical benefits. These outcomes are in direct alignment with the Sustainable Development Goals, particularly by promoting affordable and clean energy (SDG 7), fostering innovation in infrastructure (SDG 9), contributing to sustainable communities (SDG 11), and supporting climate action (SDG 13).

The demonstrated 18% improvement in economic performance and up to 27% enhancement in operational efficiency validate the model’s potential. The framework successfully manages the flexibility challenge posed by renewables, turning energy hubs into valuable assets for grid stability and profitability.

Future work will focus on expanding the model to further advance sustainability, including:

• Integrating electric vehicles (EVs) to support the decarbonization of transport.
• Incorporating demand-side management programs to further enhance system flexibility and efficiency.
• Developing a comprehensive planning model that includes the lifecycle costs of hub components to optimize long-term investment in clean energy infrastructure.

Analysis of Sustainable Development Goals (SDGs) in the Article

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

• SDG 7: Affordable and Clean Energy

  The article’s core focus is on improving the management of electrical and thermal energy through smart grids and energy hubs. It emphasizes the transition from fossil fuels to renewable energy sources (RESs) like wind turbines and bio-waste units, and the use of energy storage systems to ensure a stable and efficient energy supply. This directly aligns with ensuring access to affordable, reliable, sustainable, and modern energy.

• SDG 9: Industry, Innovation, and Infrastructure

  The paper proposes an innovative “two-layer energy management method” and a “two-stage optimization model” to build a more resilient and efficient energy infrastructure. The integration of advanced technologies such as smart grids, energy hubs, hydrogen storage (HS), compressed-air energy storage (CAES), and thermal energy storage (TES) represents a significant upgrade to technological capabilities and infrastructure, fostering innovation in the energy sector.

• SDG 11: Sustainable Cities and Communities

  By proposing systems that can “mitigate emissions” and utilize “environmental waste” through bio-waste units (BUs), the article contributes to reducing the adverse environmental impact of energy systems, which is a key aspect of making cities and human settlements more sustainable.

• SDG 12: Responsible Consumption and Production

  The research aims to enhance operational and energy efficiency, directly contributing to more sustainable consumption and production patterns. The use of BUs to convert “environmental waste” into energy is a clear example of reducing, reusing, and recycling, which is central to this goal.

• SDG 13: Climate Action

  The primary motivation stated in the article is to “mitigate emissions” by “transitioning from fossil fuels to renewable energy sources (RESs).” The entire proposed framework is a strategic approach to integrating climate change mitigation measures into the operation of energy grids.

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

1. SDG 7: Affordable and Clean Energy

   • Target 7.2: By 2030, increase substantially the share of renewable energy in the global energy mix. The article directly supports this by integrating renewable sources like wind turbines (WT) and bio-waste units (BU) into the energy system.
   • Target 7.3: By 2030, double the global rate of improvement in energy efficiency. The article explicitly mentions achieving an “18-27% enhancement in operational efficiency” and a 20% reduction in energy loss.
   • Target 7.a: By 2030, enhance international cooperation to facilitate access to clean energy research and technology… and promote investment in energy infrastructure and clean energy technology. The paper itself is a form of scientific research that promotes investment in and the adoption of clean energy technologies like smart grids, energy hubs, and various storage systems.

2. SDG 9: Industry, Innovation, and Infrastructure

   • Target 9.4: By 2030, upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies. The proposed model is designed to upgrade energy infrastructure with smart, clean technologies (RES, storage) to improve efficiency and sustainability.
   • Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries. The research presents a novel optimization framework and a hybrid solver, contributing to the scientific and technological advancement of energy management systems.

3. SDG 11: Sustainable Cities and Communities

   • Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management. The article’s inclusion of “bio-waste units (BUs)” that generate energy from “environmental waste” directly addresses sustainable waste management.

4. SDG 12: Responsible Consumption and Production

   • Target 12.2: By 2030, achieve the sustainable management and efficient use of natural resources. The model’s success in reducing energy loss and improving operational efficiency demonstrates a more efficient use of energy resources.
   • Target 12.5: By 2030, substantially reduce waste generation through prevention, reduction, recycling and reuse. The use of BUs to convert waste into energy is a direct application of this principle.

5. SDG 13: Climate Action

   • Target 13.2: Integrate climate change measures into national policies, strategies and planning. The proposed energy management framework is a clear example of a strategy that integrates climate mitigation (use of RES) into the operational planning of energy systems.

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

• For Target 7.2 (Increase renewable energy share):

  • Implied Indicator: The share of renewable energy sources (WT and BU with capacities of 0.5 MW each) in the total energy generation of the described grid.

• For Target 7.3 (Improve energy efficiency):

  • Mentioned Indicator: An “18-27% enhancement in operational efficiency.”
  • Mentioned Indicator: A 20% reduction in “energy loss.”

• For Target 9.4 (Upgrade infrastructure for sustainability):

  • Mentioned Indicator: An “18% improvement in economic performance.”
  • Mentioned Indicator: The reduction of the “flexibility cost,” which measures the system’s ability to handle the variability of renewables.

• For Target 11.6 (Reduce environmental impact/manage waste):

  • Implied Indicator: The volume of environmental waste processed by the bio-waste units to generate energy, contributing to waste reduction.

• For Target 12.2 (Efficient use of resources):

  • Mentioned Indicator: The 20% reduction in “energy loss” in energy networks.

• For Target 13.2 (Integrate climate change measures):

  • Implied Indicator: The successful implementation of the proposed energy management strategy, which is designed to “mitigate emissions” by prioritizing renewable energy sources.

4. Table of SDGs, Targets, and Indicators

SDGs	Targets	Indicators
SDG 7: Affordable and Clean Energy	7.2 Increase substantially the share of renewable energy in the global energy mix.	Share of renewable energy (WT, BU) in the grid’s energy mix.
	7.3 Double the global rate of improvement in energy efficiency.	18-27% enhancement in operational efficiency; 20% reduction in energy loss.
SDG 9: Industry, Innovation, and Infrastructure	9.4 Upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies.	18% improvement in economic performance; Reduction of flexibility cost.
	9.5 Enhance scientific research, upgrade the technological capabilities of industrial sectors.	Development and application of the two-layer energy management framework and hybrid HBMO+ABC algorithm.
SDG 11: Sustainable Cities and Communities	11.6 Reduce the adverse per capita environmental impact of cities, including… waste management.	Utilization of environmental waste by bio-waste units (BUs).
SDG 12: Responsible Consumption and Production	12.2 Achieve the sustainable management and efficient use of natural resources.	20% reduction in energy loss in energy networks.
	12.5 Substantially reduce waste generation through… recycling and reuse.	Conversion of environmental waste to energy via bio-waste units.
SDG 13: Climate Action	13.2 Integrate climate change measures into… strategies and planning.	Implementation of the energy management strategy designed to mitigate emissions by using RES.

Source: nature.com