Optimal sizing of thermal energy storage systems for CHP plants considering specific investment costs: A case study

Benalcazar, Pablo

doi:10.1016/j.energy.2021.121323

Cited by 34 publications

(17 citation statements)

References 36 publications

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“…As described in Section 1, this work expands the scope of our previous study [20] by (a) modeling and incorporating energy and electricity prices uncertainties and (b) investigating the economic and operational impacts of future energy and carbon prices on the operation of a coal-fired CHP system with thermal energy storage. Furthermore, the model used in the present study is an adapted version of the mixed-integer linear programming approach described in [21,22].…”

Section: Energy System Modelmentioning

confidence: 98%

Assessing the Effects of Uncertain Energy and Carbon Prices on the Operational Patterns and Economic Results of CHP Systems

2021

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In the power and heat sectors, the uncertainty of energy and carbon prices plays a decisive role in the rationale for decommissioning/repurposing coal-fired CHP (combined heat and power) systems and on investment decisions of energy storage units. Therefore, there is a growing need for advanced methods that incorporate the stochastic disturbances of energy and carbon emission prices into the optimization process of an energy system. In this context, this paper proposes an integrated method for investigating the effects of uncertain energy and carbon prices on the operational patterns and financial results of CHP systems with thermal energy storage units. The approach combines mathematical programming and Monte Carlo simulation. The computational process generates feasible solutions for profit maximization considering the technical constraints of the CHP system and the variation of energy and carbon emission prices. Four scenarios are established to compare the operational patterns and economic performance of a CHP system in 2020 and 2030. Results show that in 2020, there is an 80% probability that the system’s annual profit will be less than or equal to €30.98 M. However, at the same probability level, the annual profit in 2030 could fall below €11.88 M. Furthermore, the scenarios indicate that the incorporation of a thermal energy storage unit leads to higher expected profits (€0.74 M in 2020 and €0.71 M in 2030). This research shows that coal-fired CHP plant operators will face costly risks and potentially greater challenges in the upcoming years with the increasing regulatory and financial pressure on CO2 emissions and the EU’s plan of phasing out fossil fuels from electricity and heat generation.

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Section: Energy System Modelmentioning

confidence: 98%

Assessing the Effects of Uncertain Energy and Carbon Prices on the Operational Patterns and Economic Results of CHP Systems

2021

Self Cite

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“…The maintenance costs are defined as a fixed proportion of the investment costs and are calculated following the same structures, see Equation (9). Operational costs consist of fuel consumption and unit startup costs, see Equation (10).…”

Section: Optimization Model 221 Objective Functionmentioning

confidence: 99%

“…In many studies, the optimization of a combined CHP-TES system often focuses on economic aspects, taking into account the cost of CO 2 emissions. Benalcazar [10] proposed an optimization method based on the economic performance for the optimal sizing of how water TES, integrated in an existing CHP plant considering specific investment cost and different carbon prices. His analysis showed that the integration of the TES units can save operational cost and decrease the use of the heat-only boiler, which reduces fuel consumption and decreases CO 2 emissions.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimal Integration of Solar Heating and Heat Storage into Existing Fossil Fuel-Based Heat and Power Production Systems

Wang

Blondeau

2022

Energies

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Increasing the share of Renewable energy sources in District Heating (DH) systems is of great importance to mitigate their CO2 emissions. The combined integration of Solar Thermal Collectors (STC) and Thermal Energy Storage (TES) into existing Combined Heat and Power (CHP) systems can be a very cost-effective way to do so. This paper aims at finding the optimal design of STC and TES systems integrated in existing CHP’s considering two distinct objectives: economic profitability and environmental impact. To do so, we developed a three-stage framework based on Pareto-optimal solutions generated by multi-objective optimization, a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-entropy method to select the optimal solution, followed by the definition of final Operation strategy. We proposed relevant improvement of the state-of-the-art models used in similar analysis. We also applied the proposed methodology to the case of a representative, 12 MWth CHP plant. Our results show that, while the addition of TES or STC alone results in limited performances and/or higher costs, both the cost and the CO2 emissions can be reduced by integrating the optimal combination of STC and TES. For the selected, optimal solution, carbon emissions are reduced by 10%, while the Annual Total Cost (ATC) is reduced by 3%. It also improves the operational flexibility and the efficiency by peak load shaving, load valley filling and thus by decreasing the peak load boiler operation. Compared to the addition of STC alone, the use of TES results in an increased efficiency, from 88% to 92%. The optimal share of STC is then increased from 7% to 10%.

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“…Inspired by the classical economic dispatch of power systems, various studies have suggested different optimisation techniques to plan, schedule, and control the BESS in grid-connected microgrids. These studies have formulated the operation of the BESS as a dispatch optimisation problem and solved it using Linear Programming (LP) [2,3], Mixed Integer Programming (MIP) [4], Mixed-Integer Linear Programming (MILP) [5][6][7], and Mixed-Integer Nonlinear Programming (MINLP) [8,9]. In Chen et al [2], a general algebraic modelling system (GAMS) was developed and solved by CPLEX solver and then tested in a physical system based in Taiwan.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for Energy-Storage Systems in Grid-Connected Microgrids: An Investigation of Online vs. Offline Implementation

et al. 2021

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Grid-connected microgrids consisting of renewable energy sources, battery storage, and load require an appropriate energy management system that controls the battery operation. Traditionally, the operation of the battery is optimised using 24 h of forecasted data of load demand and renewable energy sources (RES) generation using offline optimisation techniques, where the battery actions (charge/discharge/idle) are determined before the start of the day. Reinforcement Learning (RL) has recently been suggested as an alternative to these traditional techniques due to its ability to learn optimal policy online using real data. Two approaches of RL have been suggested in the literature viz. offline and online. In offline RL, the agent learns the optimum policy using predicted generation and load data. Once convergence is achieved, battery commands are dispatched in real time. This method is similar to traditional methods because it relies on forecasted data. In online RL, on the other hand, the agent learns the optimum policy by interacting with the system in real time using real data. This paper investigates the effectiveness of both the approaches. White Gaussian noise with different standard deviations was added to real data to create synthetic predicted data to validate the method. In the first approach, the predicted data were used by an offline RL algorithm. In the second approach, the online RL algorithm interacted with real streaming data in real time, and the agent was trained using real data. When the energy costs of the two approaches were compared, it was found that the online RL provides better results than the offline approach if the difference between real and predicted data is greater than 1.6%.

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Optimal sizing of thermal energy storage systems for CHP plants considering specific investment costs: A case study

Cited by 34 publications

References 36 publications

Assessing the Effects of Uncertain Energy and Carbon Prices on the Operational Patterns and Economic Results of CHP Systems

Assessing the Effects of Uncertain Energy and Carbon Prices on the Operational Patterns and Economic Results of CHP Systems

Multi-Objective Optimal Integration of Solar Heating and Heat Storage into Existing Fossil Fuel-Based Heat and Power Production Systems

Reinforcement Learning for Energy-Storage Systems in Grid-Connected Microgrids: An Investigation of Online vs. Offline Implementation

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