Towards Energysheds: A Technical Definition and Cooperative Framework for Optimizing Future Power System Operations

Core Concepts

This work proposes a mathematical definition of energysheds and introduces an analytical framework for studying energyshed concepts within the context of future electric power system operations.

Abstract

The paper introduces the concept of energysheds, which are local energy communities within geographical areas where energy system objectives and constraints are determined and between which energy can be actively exchanged. The authors propose a mathematical definition of an energyshed and analyze the factors that impact a community's ability to achieve energyshed policy incentives within a larger connected power grid, as well as the tradeoffs associated with different spatial policy requirements.
The paper presents two key optimization problems:

(P1) - Minimizes system costs while adhering to energyshed policy requirements and physical infrastructure constraints.
(P2) and (P4) - Determine the optimal energyshed policy requirements that maximize the minimum local generation ratio across all energysheds, while considering system-wide costs.

The authors show that the non-convex problem (P2) can be solved to global optimality by reformulating it as a sequence of convex problems. They also demonstrate that the generalized problem (P4) can be solved efficiently by leveraging quasi-linearity and parametric optimization techniques.
The numerical case study on the IEEE 39-bus system illustrates the impact of spatial aggregation of energyshed boundaries on the tradeoffs between local generation ratio requirements and system-wide capacity costs. The results provide insights that can inform policymakers in designing effective energyshed policies.

Stats

"The goal of the objective function (8a) is to maximize the minimum value of Xk across all energysheds."
"For a given value of τ, we can evaluate the objective function (12a) as f(τ) = τ - 1/ζ f0(yτ), where yτ denotes the solution of a specific instance of the convex problem (P1) parameterized by τ."

Quotes

"Clearly, there is an interplay between local decisions, regional costs, and global policy objectives and impacts."
"Thus, as far as the authors are aware, this paper is the first attempt to analyze and better understand the role of the power network in enabling or limiting local energyshed objectives."
"Proposition 4. If f0 is convex, then an ε-optimal solution to the non-convex problem (P4) can found by solving a sequence of convex problems of the form (P1) by fixing τ."

Key Insights Distilled From

Towards Energysheds: A Technical Definition and Cooperative Framework for Future Power System Operations

by Dakota Hamil... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2311.16300.pdf

Deeper Inquiries

How can the proposed energyshed framework be extended to consider multi-energy systems (e.g., district heating, transportation) beyond just the electric power system?

The extension of the energyshed framework to encompass multi-energy systems involves integrating various energy vectors such as electricity, heat, and transportation fuels. This extension would require a holistic approach to optimize the interactions between these systems, considering their interdependencies and potential synergies. Here are some key steps to extend the framework:

Integration of Energy Vectors: The framework should incorporate the generation, distribution, and consumption of electricity, heat, and transportation fuels. This integration would enable a comprehensive analysis of energy flows and requirements across different sectors.

Modeling Energy Conversion: Different energy carriers need to be converted and utilized efficiently within the system. Models should be developed to represent the conversion processes, such as power-to-heat or power-to-gas technologies, to ensure optimal utilization of resources.

Incorporating Demand-Side Management: Demand-side management strategies should be included to optimize energy consumption patterns based on the availability of different energy sources. This could involve demand response programs for heating and cooling systems or smart charging for electric vehicles.

Infrastructure Planning: The framework should consider the infrastructure requirements for multi-energy systems, including the development of integrated networks for energy distribution and storage. This would involve assessing the capacity of existing infrastructure to support multi-energy operations.

Policy and Regulatory Considerations: Policies and regulations governing multi-energy systems need to be integrated into the framework. This includes incentives for renewable energy integration, carbon pricing mechanisms, and standards for energy efficiency.

By extending the energyshed framework to multi-energy systems, stakeholders can gain a more comprehensive understanding of energy interactions and optimize resource utilization across different sectors.

How can the potential economic, environmental, and social implications of different energyshed policy designs be incorporated into the optimization framework?

Incorporating the economic, environmental, and social implications of energyshed policy designs into the optimization framework is crucial for ensuring sustainable and equitable outcomes. Here's how these considerations can be integrated:

Cost-Benefit Analysis: The optimization framework should include economic factors such as investment costs, operational expenses, and potential revenue streams from energy trading. Cost-benefit analysis can help evaluate the financial viability of different policy designs.

Environmental Impact Assessment: Environmental considerations, such as greenhouse gas emissions, air quality, and resource depletion, should be quantified and integrated into the optimization model. This could involve incorporating emission factors for different energy sources and technologies.

Social Equity Metrics: Social implications, including access to energy services, affordability, and community engagement, should be quantified and included in the optimization framework. Metrics for social equity can be defined, such as energy access for marginalized communities or job creation in the renewable energy sector.

Multi-Objective Optimization: The optimization framework can be designed as a multi-objective model that considers economic, environmental, and social objectives simultaneously. This approach allows decision-makers to explore trade-offs and identify solutions that balance multiple criteria.

Scenario Analysis: Scenario analysis can be used to assess the sensitivity of different policy designs to economic, environmental, and social factors. By evaluating various scenarios, decision-makers can understand the robustness of their policies under different conditions.

By incorporating economic, environmental, and social implications into the optimization framework, stakeholders can make informed decisions that promote sustainability and social welfare in energyshed policy designs.

Can the insights from this work on energyshed design be applied to other resource-constrained systems, such as water or food distribution networks, to promote more equitable and sustainable outcomes?

The insights from energyshed design can indeed be applied to other resource-constrained systems, such as water or food distribution networks, to promote equitable and sustainable outcomes. Here's how these insights can be translated to other domains:

Resource Optimization: Similar to energysheds, water and food distribution networks can benefit from optimizing the allocation and utilization of resources. Models can be developed to minimize waste, reduce inefficiencies, and ensure equitable access to resources.

Spatial Analysis: Just as energysheds define geographic boundaries for energy operations, water and foodsheds can delineate areas where resources are sourced, distributed, and consumed. Spatial analysis can help identify optimal locations for infrastructure and resource management.

Policy Design: Lessons from energyshed policy design, such as cooperative frameworks and multi-objective optimization, can be applied to water and food systems. Policies that balance economic, environmental, and social objectives can lead to more sustainable resource management.

Community Engagement: Engaging local communities in the management of water and food resources, similar to energy communities, can enhance sustainability and resilience. Community-based approaches can foster ownership, participation, and innovation in resource-constrained systems.

Techno-Economic Analysis: Conducting techno-economic analysis, as done in energyshed design, can help evaluate the costs and benefits of different interventions in water and food networks. This analysis can guide decision-making towards cost-effective and sustainable solutions.

By adapting the insights and methodologies from energyshed design to water and food distribution networks, stakeholders can address resource constraints, promote equity, and enhance the sustainability of essential systems.