This paper reports the results of the adoption of a probabilistically defined communication structure in a special algorithm coined as EPSO -Evolutionary Particle Swarm Optimization, which is classified as an evolutionary algorithm using a particle movement rule as the recombination operator. Alternatively, EPSO may be seen as an algorithm of the family of PSO (Particle Swarm Optimization) but with a self-adaptive mechanism applied to make the weights of the movement rule evolve improving the performance of the algorithm. The paper presents results showing that a probabilistically controlled communication (to the particles of a swarm) of the location of the best-sofar point leads to better convergence and that the optimal value of the probability of communication depends on the topology of the surface being searched. Also, full communication (similar to classical PSO) has in all cases been shown to be worse than probabilistically constrained communication. This is demonstrated by comparing results in different test functions and also in the application of EPSO to an industrially relevant application -the reactive power planning in large scale power systems.
The participation of wind energy in electricity markets requires providing a forecast for future energy production of a wind generator, whose value will be its scheduled energy. Deviations from this schedule because of prediction errors could imply the payment of imbalance costs. In order to decrease these costs, a joint operation between a wind farm and a hydro-pump plant is proposed; the hydro-pump plant changes its production to compensate wind power prediction errors. In order to optimize this operation, the uncertainty of the wind power forecast is modeled and quantified. This uncertainty is included in an optimization problem that shifts the production of the hydro-pump plant in an optimal way, aiming at reducing the imbalance costs. The result of such a method is profitable for both participants, the wind farm and the hydro-pump plant. A realistic test case is used to evaluate the proposed method.
This work evaluates the prefeasibility of energy from waste projects in Colombia under the guidelines of Law 1715. That piece of legislation proposes tax incentives for non-conventional energy initiatives, such as deductions of up to 50% on the investment through income tax, VAT exemption, tariff exemption, and accelerated depreciation of assets. Pasto, Colombia, was selected as the case study. Subsequently, incineration, gasification, anaerobic digestion, and landfill gas technologies were evaluated. The potential of electric power generation from municipal solid waste (MSW) of each conversion technology was estimated with mathematical models. Additionally, the economic evaluation considered five cases that combine loan options, accelerated depreciation, and income deductions. Finally, the prefeasibility analysis of each case and technology was based on the internal rate of return (IRR) and levelized cost of electricity (LCOE). The results reveal that only anaerobic digestion and landfill gas technologies constitute viable projects in case of traditional investment with and without loans, because they present IRRs greater than the discount rate, of 6.8%. However, by including the incentives in Law 1715 in the economic evaluation, IRRs of 11.18%, 7.96%, 14.27%, and 13.59% were obtained for incineration, gasification, anaerobic digestion, and landfill gas, respectively. These results make all four technologies feasible in this context.
Abstract:Wind energy has seen a tremendous growth for electricity generation worldwide and reached 456 GW by the end of June 2016. According to the World Wind Energy Association, global wind power will reach 500 GW by the end of 2016. Africa is a continent that possesses huge under-utilized wind potentials. Some African countries, e.g., Morocco, Egypt, Tunisia and South Africa, have already adopted wind as an alternative power generation source in their energy mix. Among these countries, South Africa has invested heavily in wind energy with operational wind farms supplying up to 26,000 GWh annually to the national grid. However, two African countries, i.e., Cameroon and Nigeria, have vast potentials, but currently are lagging behind in wind energy development. For Nigeria, there is slow implementation of renewable energy policy, with no visible operational wind farms; while Cameroon does not have any policy plan for wind power. These issues are severely hindering both direct foreign and local investments into the electricity sector. Cameroon and Nigeria have huge wind energy potentials with similar climatic conditions and can benefit greatly from the huge success recorded in South Africa in terms of policy implementation, research, development and technical considerations. Therefore, this paper reviews the wind energy potentials, policies and future renewable energy road-maps in Cameroon and Nigeria and identifies their strength and weakness, as well as providing necessary actions for future improvement that South Africa has already adopted.
Power system operators must schedule the available generation resources required to achieve an economical, reliable, and secure energy production in power systems. This is usually achieved by solving a security-constrained unit commitment (SCUC) problem. Through a SCUC the System Operator determines which generation units must be on and off-line over a time horizon of typically 24 h. The SCUC is a challenging problem that features high computational cost due to the amount and nature of the variables involved. This paper presents an alternative formulation to the SCUC problem aimed at reducing its computational cost using sensitivity factors and user cuts. Power Transfer Distribution Factors (PTDF) and Line Outage Distribution Factors (LODF) sensitivity factors allow a fast computation of power flows (in normal operative conditions and under contingencies), while the implementation of user cuts reduces computational burden by considering only biding N-1 security constraints. Several tests were performed with the IEEE RTS-96 power system showing the applicability and effectiveness of the proposed modelling approach. It was found that the use of Linear Sensitivity Factors (LSF) together with user cuts as proposed in this paper, reduces the computation time of the SCUC problem up to 97% when compared with its classical formulation. Furthermore, the proposed modelling allows a straightforward identification of the most critical lines in terms of the overloads they produce in other elements after an outage, and the number of times they are overloaded by a fault. Such information is valuable to system planners when deciding future network expansion projects.
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