What do high penetrations of wind power mean for gas generation?

Dependency on thermal generation and continued wind power growth in Europe due to renewable energy and greenhouse gas emissions targets has resulted in an interesting set of challenges for power systems. The variability of wind power impacts dispatch and balancing by grid operators, power plant operations by generating companies and market wholesale costs. This paper quantifies the effects of high wind power penetration on power systems with a dependency on gas generation using a realistic unit commitment and economic dispatch model. The test system is analyzed under two scenarios, with and without wind, over one year. The key finding of this preliminary study is that despite increased ramping requirements in the wind scenario, the unit cost of electricity due to sub-optimal operation of gas generators does not show substantial deviation from the no wind scenario.

Optimal Scheduling Methods to Integrate Plug-In Electric Vehicles with the Power System: A Review

The introduction of the Tesla in 2008 has demonstrated to the public of the potential of electric vehicles in terms of reducing fuel consumption and green-house gas from the transport sector. It has brought electric vehicles back into the spotlight worldwide at a moment when fossil fuel prices were reaching unexpected high due to increased demand and strong economic growth. The energy storage capabilities from of fleets of electric vehicles as well as the potentially random discharging and charging offers challenges to the grid in terms of operation and control. Optimal scheduling strategies are key to integrating large numbers of electric vehicles and the smart grid. In this paper, state-of-the-art optimization methods are reviewed on scheduling strategies for the grid integration with electric vehicles. The paper starts with a concise introduction to analytical charging strategies, followed by a review of a number of classical numerical optimization methods, including linear programming, non-linear programming, dynamic programming as well as some other means such as queuing theory. Meta-heuristic techniques are then discussed to deal with the complex, high-dimensional and multi-objective scheduling problem associated with stochastic charging and discharging of electric vehicles. Finally, future research directions are suggested.

Differential evolution combined with clonal selection for dynamic economic dispatch

Dynamic economic load dispatch (DELD) is one of the most important steps in power system operation. Various optimisation algorithms for solving the problem have been developed; however, due to the non-convex characteristics and large dimensionality of the problem, it is necessary to explore new methods to further improve the dispatch results and minimise the costs. This article proposes a hybrid differential evolution (DE) algorithm, namely clonal selection-based differential evolution (CSDE), to solve the problem. CSDE is an artificial intelligence technique that can be applied to complex optimisation problems which are for example nonlinear, large scale, non-convex and discontinuous. This hybrid algorithm combines the clonal selection algorithm (CSA) as the local search technique to update the best individual in the population, which enhances the diversity of the solutions and prevents premature convergence in DE. Furthermore, we investigate four mutation operations which are used in CSA as the hyper-mutation operations. Finally, an efficient solution repair method is designed for DELD to satisfy the complicated equality and inequality constraints of the power system to guarantee the feasibility of the solutions. Two benchmark power systems are used to evaluate the performance of the proposed method. The experimental results show that the proposed CSDE/best/1 approach significantly outperforms nine other variants of CSDE and DE, as well as most other published methods, in terms of the quality of the solution and the convergence characteristics.

A hybrid harmony search with arithmetic crossover operation for economic dispatch

Economic dispatch (ED) problems often exhibit non-linear, non-convex characteristics due to the valve point effects. Further, various constraints and factors, such as prohibited operation zones, ramp rate limits and security constraints imposed by the generating units, and power loss in transmission make it even more challenging to obtain the global optimum using conventional mathematical methods. Meta-heuristic approaches are capable of solving non-linear, non-continuous and non-convex problems effectively as they impose no requirements on the optimization problems. However, most methods reported so far mainly focus on a specific type of ED problems, such as static or dynamic ED problems. This paper proposes a hybrid harmony search with arithmetic crossover operation, namely ACHS, for solving five different types of ED problems, including static ED with valve point effects, ED with prohibited operating zones, ED considering multiple fuel cells, combined heat and power ED, and dynamic ED. In this proposed ACHS, the global best information and arithmetic crossover are used to update the newly generated solution and speed up the convergence, which contributes to the algorithm exploitation capability. To balance the exploitation and exploration capabilities, the opposition based learning (OBL) strategy is employed to enhance the diversity of solutions. Further, four commonly used crossover operators are also investigated, and the arithmetic crossover shows its efficiency than the others when they are incorporated into HS. To make a comprehensive study on its scalability, ACHS is first tested on a group of benchmark functions with a 100 dimensions and compared with several state-of-the-art methods. Then it is used to solve seven different ED cases and compared with the results reported in literatures. All the results confirm the superiority of the ACHS for different optimization problems.

An efficient harmony search with new pitch adjustment for dynamic economic dispatch

A simple yet efficient harmony search (HS) method with a new pitch adjustment rule (NPAHS) is proposed for dynamic economic dispatch (DED) of electrical power systems, a large-scale non-linear real time optimization problem imposed by a number of complex constraints. The new pitch adjustment rule is based on the perturbation information and the mean value of the harmony memory, which is simple to implement and helps to enhance solution quality and convergence speed. A new constraint handling technique is also developed to effectively handle various constraints in the DED problem, and the violation of ramp rate limits between the first and last scheduling intervals that is often ignored by existing approaches for DED problems is effectively eliminated. To validate the effectiveness, the NPAHS is first tested on 10 popular benchmark functions with 100 dimensions, in comparison with four HS variants and five state-of-the-art evolutionary algorithms. Then, NPAHS is used to solve three 24-h DED systems with 5, 15 and 54 units, which consider the valve point effects, transmission loss, emission and prohibited operating zones. Simulation results on all these systems show the scalability and superiority of the proposed NPAHS on various large scale problems.

Economic Stability and Economic Governance in the Euro Area:What the European Crisis Can Teach on the Limits of Economic Integration

The recent European economic crisis has dramatically exposed the failures of
the various institutional mechanisms in place to maintain economic stability
in Europe, and has unveiled the difficulty in achieving international coordination
on fiscal and financial stability policies. Drawing on the European
experience, this article analyzes the concept of economic stability in international
law and highlights the peculiar problems connected to its maintenance
or promotion. First, we demonstrate that policies that safeguard and
protect economic stability are largely regulated and managed at the national
level, due to their inextricable relationship with the exercise of national political
power. Until recently, more limited levels of pan-European integration
did not make the coordination of economic stability policies seem necessary.
However, a much deeper level of economic integration makes it very difficult
to tackle an international economic crisis through national responses. If EU
Member states wish to maintain and deepen economic integration, they
must accept an erosion of sovereignty over their economic stability policies.
This will not only deprive states of a fundamental anchor of political power,
but also create a challenge for the maintenance of democratic control over
economic policies. Second, this article argues that soft law approaches are
likely ineffective in enforcing the regulatory disciplines required to ensure
economic stability.

Integrated techno-economic and environmental assessments of sixty scenarios for co-firing biomass with coal and natural gas

Displacement of fossil fuel-based power through biomass co-firing could reduce the greenhouse gas (GHG) emissions from fossil fuels. In this study, data-intensive techno-economic models were developed to evaluate different co-firing technologies as well as the configurations of these technologies. The models were developed to study 60 different scenarios involving various biomass feedstocks (wood chips, wheat straw, and forest residues) co-fired either with coal in a 500 MW subcritical pulverized coal (PC) plant or with natural gas in a 500 MW natural gas combined cycle (NGCC) plant to determine their technical potential and costs, as well as to determine environmental benefits. The results obtained reveal that the fully paid-off coal-fired power plant co-fired with forest residues is the most attractive option, having levelized costs of electricity (LCOE) of $53.12–$54.50/MW h and CO2 abatement costs of $27.41–$31.15/tCO2. When whole forest chips are co-fired with coal in a fully paid-off plant, the LCOE and CO2 abatement costs range from $54.68 to $56.41/MW h and $35.60 to $41.78/tCO2, respectively. The LCOE and CO2 abatement costs for straw range from $54.62 to $57.35/MW h and $35.07 to $38.48/tCO2, respectively.

Economic optimization of wind farms in function of the cost of energy produced

Esta tese apresenta um estudo sobre otimização económica de parques eólicos, com o objetivo de obter um algoritmo para otimização económica de parques eólicos através do custo da energia produzida. No estudo utilizou-se uma abordagem multidisciplinar. Inicialmente, apresentam-se as principais tecnologias e diferentes arquiteturas utilizadas nos parques eólicos. Bem como esquemas de funcionamento e gestão dos parques. São identificadas variáveis necessárias e apresenta-se um modelo dimensionamento para cálculo dos custos da energia produzida, tendo-se dado ênfase às instalações onshore e ligados a rede elétrica de distribuição. É feita uma análise rigorosa das características das topologias dos aerogeradores disponíveis no mercado, e simula-se o funcionamento de um parque eólico para testar a validade dos modelos desenvolvidos. Também é implementado um algoritmo para a obtenção de uma resposta otimizada para o ciclo de vida económico do parque eólico em estudo. A abordagem proposta envolve algoritmos para otimização do custo de produção com multiplas funções objetivas com base na descrição matemática da produção de eletricidade. Foram desenvolvidos modelos de otimização linear, que estabelece a ligação entre o custo económico e a produção de eletricidade, tendo em conta ainda as emissões de CO2 em instrumentos de política energética para energia eólica. São propostas expressões para o cálculo do custo de energia com variáveis não convencionais, nomeadamente, para a produção variável do parque eólico, fator de funcionamento e coeficiente de eficiência geral do sistema. Para as duas últimas, também é analisado o impacto da distribuição do vento predominante no sistema de conversão de energia eólica. Verifica-se que os resultados obtidos pelos algoritmos propostos são similares às obtidas por demais métodos numéricos já publicados na comunidade científica, e que o algoritmo de otimização económica sofre influência significativa dos valores obtidos dos coeficientes em questão. Finalmente, é demonstrado que o algoritmo proposto (LCOEwso) é útil para o dimensionamento e cálculo dos custos de capital e O&M dos parques eólicos com informação incompleta ou em fase de projeto. Nesse sentido, o contributo desta tese vem ser desenvolver uma ferramenta de apoio à tomada de decisão de um gestor, investidor ou ainda agente público em fomentar a implantação de um parque eólico.

Simulated annealing metaheuristic to solve the optimal power flow

The optimal power flow problem has been widely studied in order to improve power systems operation and planning. For real power systems, the problem is formulated as a non-linear and as a large combinatorial problem. The first approaches used to solve this problem were based on mathematical methods which required huge computational efforts. Lately, artificial intelligence techniques, such as metaheuristics based on biological processes, were adopted. Metaheuristics require lower computational resources, which is a clear advantage for addressing the problem in large power systems. This paper proposes a methodology to solve optimal power flow on economic dispatch context using a Simulated Annealing algorithm inspired on the cooling temperature process seen in metallurgy. The main contribution of the proposed method is the specific neighborhood generation according to the optimal power flow problem characteristics. The proposed methodology has been tested with IEEE 6 bus and 30 bus networks. The obtained results are compared with other wellknown methodologies presented in the literature, showing the effectiveness of the proposed method.

Ant colony search algorithm for the optimal power flow problem

To maintain a power system within operation limits, a level ahead planning it is necessary to apply competitive techniques to solve the optimal power flow (OPF). OPF is a non-linear and a large combinatorial problem. The Ant Colony Search (ACS) optimization algorithm is inspired by the organized natural movement of real ants and has been successfully applied to different large combinatorial optimization problems. This paper presents an implementation of Ant Colony optimization to solve the OPF in an economic dispatch context. The proposed methodology has been developed to be used for maintenance and repairing planning with 48 to 24 hours antecipation. The main advantage of this method is its low execution time that allows the use of OPF when a large set of scenarios has to be analyzed. The paper includes a case study using the IEEE 30 bus network. The results are compared with other well-known methodologies presented in the literature.

Cyber-ambient intelligent training of operators in power systems control centres

Cyber-Physical Systems and Ambient Intelligence are two of the most important and emerging paradigms of our days. The introduction of renewable sources gave origin to a completely different dimension of the distribution generation problem. On the other hand, Electricity Markets introduced a different dimension in the complexity, the economic dimension. Our goal is to study how to proceed with the Intelligent Training of Operators in Power Systems Control Centres, considering the new reality of Renewable Sources, Distributed Generation, and Electricity Markets, under the emerging paradigms of Cyber-Physical Systems and Ambient Intelligence. We propose Intelligent Tutoring Systems as the approach to deal with the intelligent training of operators in these new circumstances.

Technical and economic resources management in smart grids using heuristic optimization methods

Intensive use of Distributed Generation (DG) represents a change in the paradigm of power systems operation making small-scale energy generation and storage decision making relevant for the whole system. This paradigm led to the concept of smart grid for which an efficient management, both in technical and economic terms, should be assured. This paper presents a new approach to solve the economic dispatch in smart grids. The proposed methodology for resource management involves two stages. The first one considers fuzzy set theory to define the natural resources range forecast as well as the load forecast. The second stage uses heuristic optimization to determine the economic dispatch considering the generation forecast, storage management and demand response

The electric vehicle integration into the power system: an application to the portuguese case

Electric vehicles (EV) offer a great potential to address the integration of renewable energy sources (RES) in the power grid, and thus reduce the dependence on oil as well as the greenhouse gases (GHG) emissions. The high share of wind energy in the Portuguese energy mix expected for 2020 can led to eventual curtailment, especially during the winter when high levels of hydro generation occur. In this paper a methodology based on a unit commitment and economic dispatch is implemented, and a hydro-thermal dispatch is performed in order to evaluate the impact of the EVs integration into the grid. Results show that the considered 10 % penetration of EVs in the Portuguese fleet would increase load in 3 % and would not integrate a significant amount of wind energy because curtailment is already reduced in the absence of EVs. According to the results, the EV is charged mostly with thermal generation and the associated emissions are much higher than if they were calculated based on the generation mix.

Assessment and optimization of wind energy integration into the power systems: application to the portuguese system

The increasing integration of wind energy in power systems can be responsible for the occurrence of over-generation, especially during the off-peak periods. This paper presents a dedicated methodology to identify and quantify the occurrence of this over-generation and to evaluate some of the solutions that can be adopted to mitigate this problem. The methodology is applied to the Portuguese power system, in which the wind energy is expected to represent more than 25% of the installed capacity in a near future. The results show that the pumped-hydro units will not provide enough energy storage capacity and, therefore, wind curtailments are expected to occur in the Portuguese system. Additional energy storage devices can be implemented to offset the wind energy curtailments. However, the investment analysis performed show that they are not economically viable, due to the present high capital costs involved.