Browsing by Author "Abdollahi, Elnaz"

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Accounting for the regulation of district heating (DH) system
(2016) Wang, Haichao; Lahdelma, Risto; Abdollahi, Elnaz; Li, Xiangli
A4 Artikkeli konferenssijulkaisussa
Heat-power peak shaving and wind power accommodation of combined heat and power plant with thermal energy storage and electric heat pump
(2023-12-01) Wang, Haichao; Han, Jianbo; Zhang, Ruoyu; Sun, Mingyi; Sun, Zongyu; Hua, Pengmin; Xie, Zichan; Wang, Hai; Abdollahi, Elnaz; Lahdelma, Risto; Granlund, Katja; Teppo, Esa
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Wind power curtailment becomes a major problem in many countries. The wind accommodation mechanisms and energy saving potentials for the combined heat and power plant with thermal energy storage, electric heat pump and both should be evaluated more systematically and accurately to accommodate more wind power. Heat-power peak shaving capacities for thermal energy storage, electric heat pump and both are analyzed using a graphical method, while the operation strategy is proposed to maximize wind accommodation. A simulation model for wind power accommodation considering the energy balances and constraints of all production units is developed based on EnergyPRO. A regional energy supply system in Jilin Province, China is selected as the case study, where the influences of different peak shaving technologies and their parameters on the wind accommodation and energy saving are studied. The wind curtailment ratio is reduced from 20.31% to 13.04% and 7.51% with thermal energy storage and electric heat pump respectively, and it is further reduced to 4.21% with both. Systems with electric heat pump can save energy from 1.1% to 5.8% with different parameters of the peak shaving devices. It was found that electric heat pump has better accommodation capability than that of thermal energy storage. Wind accommodation can be improved by adding thermal energy storage to electric heat pump, but the effect gradually decreases as the storage size increases. Electric heat pump can increase the system's energy efficiency, but it is not always energy efficient by adding thermal energy storage to electric heat pump. In fact, thermal energy storage should not be too large, otherwise the system's energy efficiency will be reduced.
Modelling and optimization of combined heat and power systems
(2019) Abdollahi, Elnaz
School of Engineering | Doctoral dissertation (article-based)
Combined heat and power (CHP) production is a prominent technique for producing heat and electric power in an integrated process. While the produced heat is used locally, for district heating or for industrial processes, the electricity can be transmitted over long distances by the grid. The advantage of CHP is much higher energy efficiency than separate heat and power production. Therefore, CHP offers also significant potential for reducing emissions. At the same time, the global trend of increasing intermittent renewable energy forms is leading to imbalance between power supply and demand. While CHP production is difficult to adjust for non-coincident heat and power demand, the flexibility of a CHP system can be improved by including also separate power and heat production plants. Energy storages and power transmission between multiple areas can also improve the system flexibility and provide economic benefits. Cost efficient operation of such CHP systems can be determined by optimization models. This study presents optimization models for different CHP planning problems, with different time horizons (short to long-term), single area and multiple areas, and with heat storages, power storages, or both. Depending on which components are included to the model, dedicated decomposition-based techniques have been developed for solving each model efficiently. An integrated model is decomposed into sub-models including both local and multi-area models. The first model is a multi-period local CHP model with heat storage. This model is solved by a generic linear programming (LP) algorithm, but a special extreme point formulation is applied in the modelling. The second model is an hourly multi-area model. A two-phase de-composition method including local and network models is proposed to optimize hourly multi-area energy production with power transmission. The third model extends the decomposition model with power storages for long-term problems. Last, an iterative process based on decomposition method is developed to include also heat storages. To produce realistic test data, a method was developed to generate heat demand with proper spatial and temporal variation also for areas where hourly historical data is unavailable. The models have been validated by comparison with existing solution techniques for different problem sizes and time horizons. The results indicate that developed methods have high accuracy and fast solution time for long-term problems that is useful in solving hourly large-scale energy systems including thousands of variables. The speed advantage of the decomposition method improves with model size.The methods can be used for long-term planning of CHP system operation to support investment decisions, and for simulating system extension.
Optimization and Multicriteria Evaluation of Carbon-neutral Technologies for District Heating
(2019-04-30) Pinto, Giuseppe; Abdollahi, Elnaz; Capozzoli, Alfonso; Savoldi, Laura; Lahdelma, Risto
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The imperative to reduce emissions to counteract climate change has led to the use of renewables progressively in more areas. Looking at district heating, there is a growing interest in coupling current production systems and carbon-neutral technologies. This paper presents a methodology to support decision making about carbon-neutral technologies for district heating. The process is organized in two stages, the first one aims at optimizing the different carbon-neutral technologies according to an objective function and assess uncertainties and dependencies. In the second stage, the alternatives are evaluated using Stochastic Multicriteria Acceptability Analysis (SMAA), a simulation-based method specifically designed to consider imprecise information. The methodology was applied to a case-study in Torino, Italy, which simulates the city district heating network at a smaller scale, with the aim to explore strategies for replacing gas boiler with more sustainable technologies. According to preference information provided by decision makers, the most preferred alternative resulted in the introduction of a solar heat plant combined with an increase size of daily heat storage. Solar heat can benefit from incentives while reducing operational costs and emissions, maximizing the use of carbon-neutral heat thanks to the storage.
Parametric optimization of long-term multi-area heat and power production with power storage
(2018-11-13) Abdollahi, Elnaz; Wang, Haichao; Lahdelma, Risto
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This paper develops a model and optimization method for multi-area heat and power production with power transmission and storage. The objective function of the model is to minimize the operating costs of the system. The model can be used both for planning optimal system operation, and for simulating the effects of extended production, transmission and storage capacity. The proposed parametric decomposition method is fast enough to solve problems with a large number of hourly models. The parametric decomposition method works in two phases. First, the problem is decomposed into hourly local energy production models without storages and transmission. Parametric linear programming analysis is applied to these models for determining the optimal marginal operating costs as a function of power production. In the second phase, the optimal marginal cost functions are encoded as a linear transshipment network model including storages and transmission network. The network model is solved using generic sparse linear programming software. The operation of each production plant is determined based on the network solution. The decomposition method was validated by comparing it against an integrated linear programming model. The decomposition method demonstrates good accuracy and solves yearly models up to 30 times faster than the integrated model.