Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
158 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Improving operational flexibility of integrated energy system with uncertain renewable generations considering thermal inertia of buildings (2001.10371v1)

Published 25 Jan 2020 in eess.SY, cs.SY, and math.OC

Abstract: Insufficient flexibility in system operation caused by traditional "heat-set" operating modes of combined heat and power (CHP) units in winter heating periods is a key issue that limits renewable energy consumption. In order to reduce the curtailment of renewable energy resources through improving the operational flexibility, a novel optimal scheduling model based on chance-constrained programming (CCP), aiming at minimizing the lowest generation cost, is proposed for a small-scale integrated energy system (IES) with CHP units, thermal power units, renewable generations and representative auxiliary equipments. In this model, due to the uncertainties of renewable generations including wind turbines and photovoltaic units, the probabilistic spinning reserves are supplied in the form of chance-constrained; from the perspective of user experience, a heating load model is built with consideration of heat comfort and inertia in buildings. To solve the model, a solution approach based on sequence operation theory (SOT) is developed, where the original CCP-based scheduling model is tackled into a solvable mixed-integer linear programming (MILP) formulation by converting a chance constraint into its deterministic equivalence class, and thereby is solved via the CPLEX solver. The simulation results on the modified IEEE 30-bus system demonstrate that the presented method manages to improve operational flexibility of the IES with uncertain renewable generations by comprehensively leveraging thermal inertia of buildings and different kinds of auxiliary equipments, which provides a fundamental way for promoting renewable energy consumption.

Citations (206)

Summary

  • The paper develops a chance-constrained programming model that integrates building thermal inertia and auxiliary storage to minimize renewable curtailment and generation costs.
  • The solution converts chance constraints into a deterministic MILP using sequence operation theory and CPLEX, ensuring reliable scheduling of uncertain renewable outputs.
  • Simulation on a modified IEEE 30-bus system demonstrates significant economic benefits and improved operational flexibility through decoupling thermal and electric subsystems.

Operational Flexibility Enhancement in Integrated Energy Systems with Uncertain Renewable Energy

The paper "Improving operational flexibility of integrated energy system with uncertain renewable generations considering thermal inertia of buildings" addresses the challenge of enhancing operational flexibility in integrated energy systems (IES) loaded with uncertain renewable energy sources. The traditional "heat-set" modes of operating combined heat and power (CHP) units create a bottleneck in this regard, limiting the system's capacity to efficiently utilize renewable energy, particularly during winter months when heating demands are high. This paper proposes a novel operational model grounded in chance-constrained programming (CCP) to minimize generation costs while enhancing system flexibility.

Key Contributions and Methodology

The contributions of this paper are threefold:

  1. Chance-Constrained Programming Model: The authors have developed a CCP-based scheduling model that incorporates the uncertainties inherent in renewable resources, such as wind turbines and photovoltaic panels, within a small-scale IES. By employing probabilistic spinning reserves as chance constraints, and accounting for the thermal inertia of buildings, the model aims to minimize curtailment of renewable energy while ensuring system reliability.
  2. Solution Approach: Utilizing sequence operation theory (SOT), the proposed model transforms chance constraints into a deterministic mixed-integer linear programming (MILP) framework. This conversion facilitates the use of the CPLEX solver, which efficiently handles CCP issues and streamlines the computation process.
  3. Simulation and Results: The paper demonstrates its methodology using a modified IEEE 30-bus system. The results indicate enhanced operational flexibility and reduced renewable energy curtailment when the model is employed. The integration of various auxiliary components, such as battery energy storage systems (BESS), heat storage tanks (HST), and electric boilers (EB), serve to decouple thermal and electric subsystems, further optimizing operational costs and flexibility.

Discussion and Implications

This paper provides a substantial contribution to the optimal operation of energy systems integrating various renewable resources. The leverage of building thermal inertia and auxiliary equipment brings forth new dimensions in system operations, particularly in the coordination between electric and heating demands—a crucial aspect overlooked in some existing literature. The research further underscores:

  • Economic and Reliable Operations: By identifying an optimal balance through probabilistic constraints, the model offers significant economic benefits, reducing generation costs by up to 61,729.55$ compared to conventional scheduling.
  • Enhanced Renewable Utilization: The strategy effectively reduces the curtailment of renewables by adjusting system operations through carefully scheduled auxiliary equipment interventions.
  • Strategic Role of Building Thermal Inertia: The inclusion of thermal inertia as a tool for energy storage and release presents a valuable method to buffer fluctuations in renewable energy production, which is particularly vital in periods of low renewable output.

Future Work and Developments

The paper suggests several avenues for future research, including extending the current methodologies to more comprehensive IES models that involve demand response strategies and electric vehicles. Further explorations into realistic scenarios incorporating energy losses and time delays could yield additional insights into the operational efficiency of integrated systems. The integration of vehicle-to-grid technologies signifies another promising direction that demands further empirical analysis to enhance system resilience and flexibility.

In conclusion, this paper offers a robust framework for addressing the challenges posed by the intermittency of renewable energies within IESs. Through advanced scheduling models and thoughtful integration of auxiliary systems, it opens pathways for more resilient, flexible, and economically viable energy networks. This approach presents a significant step toward realizing the full potential of renewable integration, offering a foundational solution to achieving sustainable energy systems.