scholarly journals Flexibility Reserve of Self-Consumption Optimized Energy Systems in the Household Sector

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3017
Author(s):  
Elias Dörre ◽  
Sebastian Pfaffel ◽  
Alexander Dreher ◽  
Pedro Girón ◽  
Svenja Heising ◽  
...  
Keyword(s):  
Unit Commitment ◽  
Programming Model ◽  
Weighted Average ◽  
Heat Pumps ◽  
Mixed Integer ◽  
Single Family ◽  
Unit Commitment Problem ◽  
Household Sector ◽  
The Future ◽  
The Individual

Energy generation and consumption in the power grid must be balanced at every single moment. Within the synchronous area of continental Europe, flexible generators and loads can provide Frequency Containment Reserve and Frequency Restoration Reserve marketed through the balancing markets. The Transmission System Operators use these flexibilities to maintain or restore the grid frequency when there are deviations. This paper shows the future flexibility potential of Germany’s household sector, in particular for single-family and twin homes in 2025 and 2030 with the assumption that households primarily optimize their self-consumption. The primary focus is directed to the flexibility potential of Electric Vehicles, Heat Pumps, Photovoltaics and Battery Storage Systems. A total of 10 different household system configurations were considered and combined in a weighted average based on the scenario framework of the German Grid Development Plan. The household generation, consumption and storage units were simulated in a mixed-integer linear programming model to create the time series for the self-consumption optimized households. This solved the unit commitment problem for each of the decentralized households in their individual configurations. Finally, the individual household flexibilities were evaluated and then aggregated to a Germany-wide flexibility profile for single-family and twin homes. The results indicate that the household sector can contribute significantly to system stabilization with an average potential of 30 negative and 3 positive flexibility in 2025. In 2030, the corresponding flexibilities potentially increase to 90 and 30 , respectively. This underlines that considerable flexibility reserves could be provided by single-family and twin homes in the future.

scholarly journals Profit-Based Unit Commitment for a GENCO Equipped with Compressed Air Energy Storage and Concentrating Solar Power Units

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 576
Author(s):  
Mostafa Nasouri Gilvaei ◽  
Mahmood Hosseini Imani ◽  
Mojtaba Jabbari Ghadi ◽  
Li Li ◽  
Anahita Golrang
Keyword(s):  
Energy Storage ◽  
Solar Power ◽  
Unit Commitment ◽  
Thermal Power ◽  
System Structure ◽  
Compressed Air ◽  
Mixed Integer ◽  
Compressed Air Energy Storage ◽  
Concentrating Solar Power ◽  
Unit Commitment Problem

With the advent of restructuring in the power industry, the conventional unit commitment problem in power systems, involving the minimization of operation costs in a traditional vertically integrated system structure, has been transformed to the profit-based unit commitment (PBUC) approach, whereby generation companies (GENCOs) perform scheduling of the available production units with the aim of profit maximization. Generally, a GENCO solves the PBUC problem for participation in the day-ahead market (DAM) through determining the commitment and scheduling of fossil-fuel-based units to maximize their own profit according to a set of forecasted price and load data. This study presents a methodology to achieve optimal offering curves for a price-taker GENCO owning compressed air energy storage (CAES) and concentrating solar power (CSP) units, in addition to conventional thermal power plants. Various technical and physical constraints regarding the generation units are considered in the provided model. The proposed framework is mathematically described as a mixed-integer linear programming (MILP) problem, which is solved by using commercial software packages. Meanwhile, several cases are analyzed to evaluate the impacts of CAES and CSP units on the optimal solution of the PBUC problem. The achieved results demonstrate that incorporating the CAES and CSP units into the self-scheduling problem faced by the GENCO would increase its profitability in the DAM to a great extent.

scholarly journals A Novel Strategy to Reduce Computational Burden of the Stochastic Security Constrained Unit Commitment Problem

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3777
Author(s):  
Cristian Camilo Marín-Cano ◽  
Juan Esteban Sierra-Aguilar ◽  
Jesús M. López-Lezama ◽  
Álvaro Jaramillo-Duque ◽  
Juan G. Villegas
Keyword(s):  
Decision Making ◽  
Power Systems ◽  
Large Scale ◽  
Unit Commitment ◽  
Reaction Times ◽  
Mixed Integer ◽  
Feasible Region ◽  
Solution Strategy ◽  
Unit Commitment Problem ◽  
Joint Strategy

The uncertainty related to the massive integration of intermittent energy sources (e.g., wind and solar generation) is one of the biggest challenges for the economic, safe and reliable operation of current power systems. One way to tackle this challenge is through a stochastic security constraint unit commitment (SSCUC) model. However, the SSCUC is a mixed-integer linear programming problem with high computational and dimensional complexity in large-scale power systems. This feature hinders the reaction times required for decision making to ensure a proper operation of the system. As an alternative, this paper presents a joint strategy to efficiently solve a SSCUC model. The solution strategy combines the use of linear sensitivity factors (LSF) to compute power flows in a quick and reliable way and a method, which dynamically identifies and adds as user cuts those active security constraints N − 1 that establish the feasible region of the model. These two components are embedded within a progressive hedging algorithm (PHA), which breaks down the SSCUC problem into computationally more tractable subproblems by relaxing the coupling constraints between scenarios. The numerical results on the IEEE RTS-96 system show that the proposed strategy provides high quality solutions, up to 50 times faster compared to the extensive formulation (EF) of the SSCUC. Additionally, the solution strategy identifies the most affected (overloaded) lines before contingencies, as well as the most critical contingencies in the system. Two metrics that provide valuable information for decision making during transmission system expansion are studied.

scholarly journals Evaluati on of long-term start up costs impact on short-term price based operational optimization of a CCGT using MILP

2019 ◽  
Vol 137 ◽  
pp. 01012
Author(s):  
Sylwia Gotzman ◽  
Paweł Ziόłkowski ◽  
Janusz Badur
Keyword(s):  
Power Plants ◽  
Unit Commitment ◽  
Real Life ◽  
Mixed Integer ◽  
Unit Commitment Problem ◽  
Operational Optimization ◽  
Start Up ◽  
Commitment Problem ◽  
Electricity Spot Market

An increasing share of the weather-dependent RES generation in the power system leads to the growing importance of flexibility of conventional power plants. They were usually designed for base load operation and it is a challenge to determine the actual long-term cycling costs, which account for an increase in maintenance and overhaul expenditures, increased forced outage rates and shortened life expectancy of the plant and components. In this paper, the overall impact of start up costs is evaluated by formulating and solving price based unit commitment problem (PBUC). The electricity spot market is considered as a measure for remunerating flexibility. This approach is applied to a real-life case study based on the 70 MWe PGE Gorzόw CCGT power plant. Different operation modes are calculated and results are used to derive a mixed integer linear programming (MILP) model to optimize the operation of the plant. The developed mathematical model is implemented in Python within the frame of the PuLP library and solved using GUROBI. Results of the application of the method to a numerical example are presented.

scholarly journals Stochastic Unit Commitment Problem, Incorporating Wind Power and an Energy Storage System

2020 ◽  
Vol 12 (23) ◽  
pp. 10100
Author(s):  
Khalid Alqunun ◽  
Tawfik Guesmi ◽  
Abdullah F. Albaker ◽  
Mansoor T. Alturki
Keyword(s):  
Energy Storage ◽  
Wind Power ◽  
Unit Commitment ◽  
Mixed Integer ◽  
Master Problem ◽  
Thermal Unit ◽  
Unit Commitment Problem ◽  
Battery Energy Storage ◽  
Commitment Problem ◽  
Battery Energy

This paper presents a modified formulation for the wind-battery-thermal unit commitment problem that combines battery energy storage systems with thermal units to compensate for the power dispatch gap caused by the intermittency of wind power generation. The uncertainty of wind power is described by a chance constraint to escape the probabilistic infeasibility generated by classical approximations of wind power. Furthermore, a mixed-integer linear programming algorithm was applied to solve the unit commitment problem. The uncertainty of wind power was classified as a sub-problem and separately computed from the master problem of the mixed-integer linear programming. The master problem tracked and minimized the overall operation cost of the entire model. To ensure a feasible and efficient solution, the formulation of the wind-battery-thermal unit commitment problem was designed to gather all system operating constraints. The solution to the optimization problem was procured on a personal computer using a general algebraic modeling system. To assess the performance of the proposed model, a simulation study based on the ten-unit power system test was applied. The effects of battery energy storage and wind power were deeply explored and investigated throughout various case studies.

An MILP Formulation for the Thermal Unit Commitment Problem Considering Start-Up and Shut-Down Power Trajectories

2014 ◽  
Vol 672-674 ◽  
pp. 493-498 ◽  
Author(s):  
Jun Deng ◽  
Hua Wei
Keyword(s):  
Unit Commitment ◽  
Mixed Integer ◽  
Test Case ◽  
Thermal Unit ◽  
Unit Commitment Problem ◽  
Binary Variables ◽  
Computational Performance ◽  
Start Up ◽  
Commitment Problem ◽  
Mixed Integer Linear Formulation

This paper presents a mixed-integer linear formulation for the thermal unit commitment problem considering the start-up and shut-down power trajectories. A realistic and accurate modeling of the unit’s operating phase is given, which includes the phases of start-up, dispatchable and shut-down. The start-up type is decided by the unit’s prior off-line time. The start-up costs and power trajectories depend on the type of start-up. A new set of binary variables is introduced to represent the dispatchable status, which can decrease the binary variables and constraints significantly. Finally, a test case study is analyzed to verify the correctness and show the computational performance of the proposed formulation.

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