Wind Energy Potential Assessment in Coastal States in Nigeria

Categories: Greenhouse gases

1. Introduction

CHP units exploit the waste heat to supply electrical and heat power simultaneously to the consumers. Employing the CHP with renewable energy resources increases the overall efficiency of the system and reduces the operating cost. Besides, the emission of greenhouse gases is reduced. This leads to tackle global warming and environmental pollution. Recently, the low voltage distribution grid has been developed to include different types of generators, renewable energy resources, storage devices, and CHP. This developed distribution grid supplies the end-users with electricity and heat in a smart approach.

The optimal management of this system draws more attention to the researchers due to its economic benefits for both users and the environment. Therefore, many sophisticated algorithms and approaches are proposed in the literature to analyse the optimization problem of the distribution grids [1- 6]. However, the CHP is not taking into account in these works.
Reference [7] proposed an optimization approach to minimize the operating cost and emission level of a microgrid (MG) including micro-turbine and fuel cell as CHP and electrical and thermal storage devices with wind and solar energy resources.

In [8], the authors presented an optimization approach to minimize operating and emission cost. They employed the same system of reference [7]. However, they did not consider any constraints in their optimization problem. Reference [9] addressed the economic dispatch of a MG based CHP to minimize the running cost, where the problem is formulated as MILP. Authors in [10] suggested an optimal energy scheduling to minimize the operating cost of MG and satisfy the electrical and thermal loads, where the MG includes photovoltaic panels, wind turbine, CHP, storage battery and electric vehicles.

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Authors in [11] presented optimal energy management to minimize the operating cost for a connected MG that includes renewable energy sources, CHP, and electrical and thermal energy storage. In [12], the optimal generation planning strategy to maximize the profit of MG that consists of wind turbines, PV, fuel cell, CHP units, tidal steam turbine are addressed. Reference [13] proposed an optimal management system to minimize the operating and carbon dioxide emission cost for MG that contains CHP, wind turbine, PV panels, and storage battery. The optimization problem was formulated as MILP, which can be solved easily.
In this paper, a developed energy management strategy is proposed for a smart low voltage distribution grid that consists of CHP, fuel cell (FC), Diesel generator (DG), wind turbines (WTs), Photovoltaic panels (PV), electric and heat storage devices, gas boiler, electric heater. The problem is formulated as multi-objective optimization, where this function is transformed into a single objective by converting the emission cost of greenhouse gases (GHG) to the monetary concept and considering the emission limitation constraints. This function can be solved directly and a single solution can be found. According to the literature, there is no study includes the model of cost function which includes the operating cost of different energy resources, exchanging heat and power with the utility grid with consideration of emission cost of carbon dioxide (CO2), sulphur dioxide (SO2), nitrogen oxide (NOx) and particulate matter (PM). Besides, a set of realistic constraints with emission limitation constraints is considered as well. Furthermore, the unit commitment strategy is employed and developed to respond to both the electrical and heat loads.

Structure of Smart Distribution Grid-Based CHP and Mathematical Model

Figure 1 shows the structure of the proposed CHP distribution grid. It includes micro-turbine (MT), FC, DG, storage devices, gas boiler, electric heater, WTs, and PV units. Besides, the grid can exchange electrical and heat energy with the utility grid. To optimize the operation of the proposed grid, each component of the system should be modelled. 

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Wind Energy Potential Assessment in Coastal States in Nigeria. (2021, Oct 31). Retrieved from

Wind Energy Potential Assessment in Coastal States in Nigeria
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