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The German government has set ambitious targets for wind energy expansion and has implemented policy schemes aimed at facilitating market deployment of wind-generated electricity. Data on German wind energy market has shown that, wind energy generating capacity is increasing correspondingly towards the targeted values. Germany is the largest wind energy market in the EU (wind energy accounting for about 10% of the total electricity consumption in Germany) with an installed capacity of about 38.2 GW onshore by the close of 2014. The success of these policies has prompted other countries globally to adopt similar support schemes for renewable energies. However, the rapid growth of wind energy generation in Germany equally faces numerous challenges. Some of these problems are inherent to the wind energy technology while others are caused by the very policies instruments used to support wind energy expansion, such as limited availability of designated areas for wind energy development, non-uniform regulations, and rising prices. The objective of this research, therefore, is to assess the impact of the German energy and environmental policies on onshore wind energy development and to explore implementation options of the German model in Cameroon.
This research has examined the German onshore wind energy sector from a policy perspective based on existing literature, semi-structured interviews with major stakeholders, a case study of Brandenburg and a survey, with the aim of investigating the acceptance of wind energy and challenges the developers are facing.
Based on the study results, it can be affirmed that the future growth of the German onshore market will come from flexible government policies, which may offer fewer incentives to investors. Indeed, the cost of electricity from renewable energy technologies in Germany is in some cases already below retail rates. There is the need, therefore, to pursue strategic programmes that enhances market integration of wind energy. Furthermore, the study results equally shows that the German feed-in tariff based support scheme in its current form, cannot be implemented in Cameroon. This is because, the purchasing power of the Cameroonian population and the economic constraints of the government, makes it difficult or even impossible to adopt the current EEG model in Cameroon where often, basic needs are subsidized.
Energy demand of continents, countries, communities and individuals will continue to increase in the phase of increasing population and improvement in the living standards of people. The attempt to meet this ever increasing demand and at the same time protect the environment has resulted in the fast growth of power generation from renewable sources of energy especially from wind through wind power plants and solar through photovoltaic power plants. This growth has been facilitated by various support schemes such as feed-in-tariff scheme, feed-in-premium and quota scheme. Further growth is expected in the future. This is because of the existing support schemes and the expectation of the emergence of improved technologies for harvesting renewable energy.
This development of power generation from renewable sources of energy although positive lead to some distinctive negative effects on the existing electrical network to which they are connected. These negative effects are known and well documented. The fluctuating nature of wind and solar radiation at any given location over a given period of observation is seen to translate into the power they feed into the power network. This fluctuating infeed requires more active management of the network by system operators so as to ensure continuous reliable power generation and delivery. Sometimes the management process lead to non-utilization of power produced by the renewables sources. Secondly, expansion and reinforcement of some existing networks are needed in other to accommodate renewable power generators. These come at a cost. Many studies and researches have been dedicated to finding solutions to these issues.
This work agrees with the use of storage systems as means of solving these issues but the question that remains unanswered is what the optimal way is. There is also a further push given to the view of installing renewable energy plants together with storage systems as a unit in this work. The main task presented in this work, however, is a concept of sizing renewable energy plant and storage systems as a unit. The resulting renewable energy plant-storage unit has the objective of supporting the electrical network to which it will be connected. Firstly the support should be by reducing the fluctuating effect from renewable production. Secondly by helping improve the load hosting capacity of the electrical network. This will be by supplying the part of the load demand leading to the reduction of the overall power drawn by connected loads from the electrical power network.
Historic data of renewable resource and also the load demand at the point or bus of connection are the drivers of this concept. With the earlier mentioned objectives and random or stochastic nature of data involved, particle swarm optimization method is employed in implementing the concept of sizing to arrive at an optimal solution of required sizes of the renewable energy plant-storage system.
The concept of sizing is based on proposing an ideal load demand that can be supplied by a utility under normal operating condition at all time. It follows that any extra demand should be supplied by the optimally sized renewable energy plant-storage unit. In this work sizing results of three scenarios presented. A single node network with three different types of the load was used in testing the effect of optimally sized renewable energy plant-storage system on an electrical network. The outcome of this test showed that the optimally sized renewable energy storage-system improved the ability of the test electrical network to support additional load hence load hosting capacity of test network was improved. The process required modelling and simulation all of which were carried out using MATLAB Simulink software.