A. Benyaghoob sani; M. Sedighizadeh; D. Sedighizadeh; R. Abbasi
Abstract
An optimal day-ahead operation of a microgrid based on coastal energy hub is presented in this paper. The proposed CEH included wind turbine, photovoltaic unit, combined cooling, heat and power, and seawater desalination. The purpose of the optimization is minimization of the operational and environmental ...
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An optimal day-ahead operation of a microgrid based on coastal energy hub is presented in this paper. The proposed CEH included wind turbine, photovoltaic unit, combined cooling, heat and power, and seawater desalination. The purpose of the optimization is minimization of the operational and environmental costs considering several technical limitations. The CEH includes an ice storage conditioner together with an energy storage system, i.e. thermal energy storage system. Particularly, the impacts of an innovative rechargeable and emerging ESS that is solar-powered compressed air energy storage is scrutinized, on the efficiency and operational and pollution costs of the CEH. It is clear that there is an intrinsic deviation between predicted and actual uncertainty variables in MG. This paper presents a bi-level stochastic optimal operation model based on risk averse strategy of information gap decision theory to overcome this information gap and to help Microgrid operator. To reduce the complexity of the proposed model, Karush-Kuhn-Tucker method is used for converting the bi-level problem into a single level. The Augmented Epsilon Constraint method is used to deals with multi objective optimization problem to harvest the maximum horizon of the uncertainties of the parameters. The proposed model implemented the Time of Use program as a price-based demand response program. Finally, the efficacy of the SPCAES for minimizing the operational cost and pollutions in the day-ahead operation is depicted by implementation of the presented model on the typical CEH.
Energy Management
F. Jabari; B. Mohammadi ivatloo; M. B. Bannae Sharifian; H. Ghaebi
Abstract
Nowadays, water and electricity are closely interdependent essential sources in human life that affect socio-economic growth and prosperity. In other words, electricity is a fundamental source to supply a seawater desalination process, while fresh water is used for cooling this power plant. Therefore, ...
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Nowadays, water and electricity are closely interdependent essential sources in human life that affect socio-economic growth and prosperity. In other words, electricity is a fundamental source to supply a seawater desalination process, while fresh water is used for cooling this power plant. Therefore, mutual vulnerability of water treatment and power generation systems is growing because of increased potable water and electricity demands especially during extremely-hot summer days. Hence, this paper presents a novel framework for optimal short-term scheduling of water-power nexus aiming to minimize total seawater desalination and electricity procurement cost while satisfying all operational constraints of conventional thermal power plants, co-producers and desalination units. Moreover, advanced adiabatic compressed air energy storage (CAES) with no need to fossil fuels can participate in energy procurement process by optimal charging during off-peak periods and discharging at peak load hours. A mixed integer non-linear programming (MINLP) problem is solved under general algebraic mathematical modeling system to minimize total water treatment cost of water only units and co-producers, total fuel cost of thermal power plants and co-generators. Ramp up and down rates, water and power generation capacities and balance criteria have been considered as optimization constraints. It is found that without co-optimization of desalination and power production plants, load-generation mismatch occurs in both water and energy networks. By incorporating CAES in water-power grids, total fuel cost of thermal units and co-producers reduce from $1222.3 and $24933.2 to $1174.8 and $24636.8, respectively. In other words, application of CAES results in $343.9 cost saving in benchmark water-power hybrid grid.
