Estudo e análise das centrais híbridas de energias renováveis

Com o aumento do preço da eletricidade e o fim dos combustíveis fósseis, associados à necessidade de Portugal reduzir a sua dependência energética do exterior, provoca a necessidade urgente de apostar nas energias renováveis. Perante este cenário, e assumindo que o custo da fatura energética, é para as empresas portuguesas um fator cada vez mais determinante para serem competitivas, devido aos aumentos consecutivos da energia nos últimos anos, bem como, a subida do imposto de valor acrescentado (IVA) de 6% para 23%. Outro aspeto importante é a eficiência energética como instrumento para reduzir os consumos de eletricidade. Com estas duas medidas: utilização de energias renováveis e o aumento da eficiência energética, são extremamente importantes para a redução da produção dos gases de efeito estufa (GEE). Consequentemente, as empresas terão de investir na produção da própria energia a partir de fontes renováveis, de modo a proporcionar um desenvolvimento sustentável, associado à redução da fatura energética. Esta dissertação propõe o dimensionamento de um sistema híbrido composto por tecnologia fotovoltaica e eólica, com e sem armazenamento de energia em baterias, adequado para reduzir uma parte dos consumos de uma empresa enquadrada no sector dos plásticos. O dimensionamento deste sistema, foi efetuado com recurso à caracterização dos consumos da empresa através da recolha de dados e leituras no local da instalação. Paralelamente, foi efetuada uma pesquisa em diversos fabricantes, de modo a identificar qual o sistema mais indicado a adotar, considerando painéis fotovoltaicos, turbinas eólicas, inversores e baterias. Com base nos dados recolhidos na empresa e referentes ao potencial eólico e solar para o distrito do Porto, em conjunto com as características técnicas dos equipamentos selecionados, foi delineado o sistema híbrido utilizando para o efeito um software de simulação e otimização de sistemas híbridos, denominado Hybrid Optimization Model for Eletric Renewable (HOMER). São apresentadas várias simulações para as diversas configurações escolhidas e estudos comparativos entre si, com o objetivo de reduzir o consumo de eletricidade da rede. Adicionalmente, foram realizadas duas configurações apenas com tecnologia fotovoltaica, de modo a efetuar uma análise comparativa entre um sistema híbrido e outro apenas com uma fonte renovável. Os resultados apresentados focaram-se no desempenho diário, mensal e anual, bem como, a produção individual de cada tecnologia evidenciada. Por último, procedeu-se ao estudo da viabilidade técnico-económica das configurações.

Estudo da Viabilidade Técnico-Económica de umm Sistema de Cogeração para um Complexo de Piscinas Interiores

A presente dissertação centrou-se no estudo técnico-económico de dois cenários futuros para a continuação de fornecimento de energia térmica a um complexo de piscinas existente na região do vale do Tâmega. Neste momento a central de cogeração existente excedeu a sua licença de utilização e necessita de ser substituída. Os dois cenários em estudo são a compra de uma nova caldeira, a gás natural, para suprir as necessidades térmicas da caldeira existente a fuelóleo, ou o uso de um sistema de cogeração compacto que poderá estar disponível numa empresa do grupo. No primeiro cenário o investimento envolvido é cerca de 456 640 € sem proveitos de outra ordem para além dos requisitos térmicos, mas no segundo cenário os resultados são bem diferentes, mesmo que tenha de ser realizado o investimento de 1 000 000 € na instalação. Para este cenário foi efetuado um levantamento da legislação nacional no que toca à cogeração, recolheram-se dados do edifício como: horas de funcionamento, número de utentes, consumos de energia elétrica, térmica, água, temperatura da água das piscinas, temperatura do ar da nave, assim como as principais características da instalação de cogeração compacta. Com esta informação realizou-se o balanço de massa e energia e criou-se um modelo da nova instalação em software de modelação processual (Aspen Plus® da AspenTech). Os rendimentos térmico e elétrico obtidos da nova central de cogeração compacta foram, respetivamente, de 38,1% e 39,8%, com uma percentagem de perdas de 12,5% o que determinou um rendimento global de 78%. A avaliação da poupança de energia primária para esta instalação de cogeração compacta foi de 19,6 % o que permitiu concluir que é de elevada eficiência. O modelo criado permitiu compreender as necessidades energéticas, determinar alguns custos associados ao processo e simular o funcionamento da unidade com diferentes temperaturas de ar ambiente (cenários de verão e inverno com temperaturas médias de 20ºC e 5ºC). Os resultados revelaram uma diminuição de 1,14 €/h no custo da electricidade e um aumento do consumo de gás natural de 62,47 €/h durante o período mais frio no inverno devido ao aumento das perdas provocadas pela diminuição da temperatura exterior. Com esta nova unidade de cogeração compacta a poupança total anual pode ser, em média, de 267 780 € admitindo um valor para a manutenção de 97 698 €/ano. Se assim for, o projeto apresenta um retorno do investimento ao fim de 5 anos, com um VAL de 1 030 430 € e uma taxa interna de rentabilidade (TIR) de 14% (positiva, se se considerar a taxa de atualização do investimento de 3% para 15 anos de vida). Apesar do custo inicial ser elevado, os parâmetros económicos mostram que o projeto tem viabilidade económica e dará lucro durante cerca de 9 anos.

Virtual Power Player Using Demand Response to Deal with Unexpected Low Wind Power Generation

Demand response is assumed as an essential resource to fully achieve the smart grids operating benefits, namely in the context of competitive markets and of the increasing use of renewable-based energy sources. Some advantages of Demand Response (DR) programs and of smart grids can only be achieved through the implementation of Real Time Pricing (RTP). The integration of the expected increasing amounts of distributed energy resources, as well as new players, requires new approaches for the changing operation of power systems. The methodology proposed in this paper aims the minimization of the operation costs in a distribution network operated by a virtual power player that manages the available energy resources focusing on hour ahead re-scheduling. When facing lower wind power generation than expected from day ahead forecast, demand response is used in order to minimize the impacts of such wind availability change. In this way, consumers actively participate in regulation up and spinning reserve ancillary services through demand response programs. Real time pricing is also applied. The proposed model is especially useful when actual and day ahead wind forecast differ significantly. Its application is illustrated in this paper implementing the characteristics of a real resources conditions scenario in a 33 bus distribution network with 32 consumers and 66 distributed generators.

Maximizing the Social Welfare of Virtual Power Players Operation in Case of Excessive Wind Power

The integration of growing amounts of distributed generation in power systems, namely at distribution networks level, has been fostered by energy policies in several countries around the world, including in Europe. This intensive integration of distributed, non-dispatchable, and natural sources based generation (including wind power) has caused several changes in the operation and planning of power systems and of electricity markets. Sometimes the available non-dispatchable generation is higher than the demand. This generation must be used; otherwise it is wasted if not stored or used to supply additional demand. New policies and market rules, as well as new players, are needed in order to competitively integrate all the resources. The methodology proposed in this paper aims at the maximization of the social welfare in a distribution network operated by a virtual power player that aggregates and manages the available energy resources. When facing a situation of excessive non-dispatchable generation, including wind power, real time pricing is applied in order to induce the increase of consumption so that wind curtailment is minimized. This method is especially useful when actual and day-ahead resources forecast differ significantly. The distribution network characteristics and concerns are addressed by including the network constraints in the optimization model. The proposed methodology has been implemented in GAMS optimization tool and its application is illustrated in this paper using a real 937-bus distribution network with 20.310 consumers and 548 distributed generators, some of them non-dispatchable and with must take contracts. The implemented scenario corresponds to a real day in Portuguese power system.

Energy resources management in three distinct time horizons considering a large variation in wind power

The intensive use of distributed generation based on renewable resources increases the complexity of power systems management, particularly the short-term scheduling. Demand response, storage units and electric and plug-in hybrid vehicles also pose new challenges to the short-term scheduling. However, these distributed energy resources can contribute significantly to turn the shortterm scheduling more efficient and effective improving the power system reliability. This paper proposes a short-term scheduling methodology based on two distinct time horizons: hour-ahead scheduling, and real-time scheduling considering the point of view of one aggregator agent. In each scheduling process, it is necessary to update the generation and consumption operation, and the storage and electric vehicles status. Besides the new operation condition, more accurate forecast values of wind generation and consumption are available, for the resulting of short-term and very short-term methods. In this paper, the aggregator has the main goal of maximizing his profits while, fulfilling the established contracts with the aggregated and external players.

SCADA House Intelligent Management for Energy Efficiency Analysis in Domestic Consumers

The implementation of smart homes allows the domestic consumer to be an active player in the context of the Smart Grid (SG). This paper presents an intelligent house management system that is being developed by the authors to manage, in real time, the power consumption, the micro generation system, the charge and discharge of the electric or plug-in hybrid vehicles, and the participation in Demand Response (DR) programs. The paper proposes a method for the energy efficiency analysis of a domestic consumer using the SCADA House Intelligent Management (SHIM) system. The main goal of the present paper is to demonstrate the economic benefits of the implemented method. The case study considers the consumption data of some real cases of Portuguese house consumption over 30 days of June of 2012, the Portuguese real energy price, the implementation of the power limits at different times of the day and the economic benefits analysis.

Modified Discrete PSO to Increase the Delivered Energy Probability in Distribution energy Systems

This paper proposes a PSO based approach to increase the probability of delivering power to any load point by identifying new investments in distribution energy systems. The statistical failure and repair data of distribution components is the main basis of the proposed methodology that uses a fuzzyprobabilistic modeling for the components outage parameters. The fuzzy membership functions of the outage parameters of each component are based on statistical records. A Modified Discrete PSO optimization model is developed in order to identify the adequate investments in distribution energy system components which allow increasing the probability of delivering power to any customer in the distribution system at the minimum possible cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 180 bus distribution network.

Dispatch of distributed Energy Resources to Provide Energy and Reserve in Smart Grids using a Particle Swarm Optimization Approach

The smart grid concept is a key issue in the future power systems, namely at the distribution level, with deep concerns in the operation and planning of these systems. Several advantages and benefits for both technical and economic operation of the power system and of the electricity markets are recognized. The increasing integration of demand response and distributed generation resources, all of them mostly with small scale distributed characteristics, leads to the need of aggregating entities such as Virtual Power Players. The operation business models become more complex in the context of smart grid operation. Computational intelligence methods can be used to give a suitable solution for the resources scheduling problem considering the time constraints. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The optimal schedule minimizes the operation costs and it is obtained using a particle swarm optimization approach, which is compared with a deterministic approach used as reference methodology. The proposed method is applied to a 33-bus distribution network with 32 medium voltage consumers and 66 distributed generation units.

Smart Grid Market Using Joint Energy and Ancillary Services Bids

The power systems operation in the smart grid context increases significantly the complexity of their management. New approaches for ancillary services procurement are essential to ensure the operation of electric power systems with appropriate levels of stability, safety, quality, equity and competitiveness. These approaches should include market mechanisms which allow the participation of small and medium distributed energy resources players in a competitive market environment. In this paper, an energy and ancillary services joint market model used by an aggregator is proposed, considering bids of several types of distributed energy resources. In order to improve economic efficiency in the market, ancillary services cascading market mechanism is also considered in the model. The proposed model is included in MASCEM – a multi-agent system electricity market simulator. A case study considering a distribution network with high penetration of distributed energy resources is presented.

Integration in MASCEM of the Joint Dispatch of Energy and Reserves Provided by Generation and Demand Resources

The provision of reserves in power systems is of great importance in what concerns keeping an adequate and acceptable level of security and reliability. This need for reserves and the way they are defined and dispatched gain increasing importance in the present and future context of smart grids and electricity markets due to their inherent competitive environment. This paper concerns a methodology proposed by the authors, which aims to jointly and optimally dispatch both generation and demand response resources to provide the amounts of reserve required for the system operation. Virtual Power Players are especially important for the aggregation of small size demand response and generation resources. The proposed methodology has been implemented in MASCEM, a multi agent system also developed at the authors’ research center for the simulation of electricity markets.

Upper Ontology for Multi-Agent Energy Systems’ Applications

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors research group has developed three multi-agent systems: MASCEM, which simulates the electricity markets; ALBidS that works as a decision support system for market players; and MASGriP, which simulates the internal operations of smart grids. To take better advantage of these systems, their integration is mandatory. For this reason, is proposed the development of an upper-ontology which allows an easier cooperation and adequate communication between them. Additionally, the concepts and rules defined by this ontology can be expanded and complemented by the needs of other simulation and real systems in the same areas as the mentioned systems. Each system’s particular ontology must be extended from this top-level ontology.

Strategic Bidding in Electricity Markets: An agent-based simulator with game theory for scenario analysis

Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players’ actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.

Constrained Consumption Shifting Management in the Distributed Energy Resources Scheduling Considering Demand Response

Demand response concept has been gaining increasing importance while the success of several recent implementations makes this resource benefits unquestionable. This happens in a power systems operation environment that also considers an intensive use of distributed generation. However, more adequate approaches and models are needed in order to address the small size consumers and producers aggregation, while taking into account these resources goals. The present paper focuses on the demand response programs and distributed generation resources management by a Virtual Power Player that optimally aims to minimize its operation costs taking the consumption shifting constraints into account. The impact of the consumption shifting in the distributed generation resources schedule is also considered. The methodology is applied to three scenarios based on 218 consumers and 4 types of distributed generation, in a time frame of 96 periods.

Incentive-based demand response programs designed by asset-light retailers for day-ahead market

Following the deregulation experience of retail electricity markets in most countries, the majority of the new entrants of the liberalized retail market were pure REP (retail electricity providers). These entities were subject to financial risks because of the unexpected price variations, price spikes, volatile loads and the potential for market power exertion by GENCO (generation companies). A REP can manage the market risks by employing the DR (demand response) programs and using its' generation and storage assets at the distribution network to serve the customers. The proposed model suggests how a REP with light physical assets, such as DG (distributed generation) units and ESS (energy storage systems), can survive in a competitive retail market. The paper discusses the effective risk management strategies for the REPs to deal with the uncertainties of the DAM (day-ahead market) and how to hedge the financial losses in the market. A two-stage stochastic programming problem is formulated. It aims to establish the financial incentive-based DR programs and the optimal dispatch of the DG units and ESSs. The uncertainty of the forecasted day-ahead load demand and electricity price is also taken into account with a scenario-based approach. The principal advantage of this model for REPs is reducing the risk of financial losses in DAMs, and the main benefit for the whole system is market power mitigation by virtually increasing the price elasticity of demand and reducing the peak demand.

Cost Allocation Model for Distribution Networks Considering High Penetration of Distributed Energy Resources

The high penetration of distributed energy resources (DER) in distribution networks and the competitiveenvironment of electricity markets impose the use of new approaches in several domains. The networkcost allocation, traditionally used in transmission networks, should be adapted and used in the distribu-tion networks considering the specifications of the connected resources. The main goal is to develop afairer methodology trying to distribute the distribution network use costs to all players which are usingthe network in each period. In this paper, a model considering different type of costs (fixed, losses, andcongestion costs) is proposed comprising the use of a large set of DER, namely distributed generation(DG), demand response (DR) of direct load control type, energy storage systems (ESS), and electric vehi-cles with capability of discharging energy to the network, which is known as vehicle-to-grid (V2G). Theproposed model includes three distinct phases of operation. The first phase of the model consists in aneconomic dispatch based on an AC optimal power flow (AC-OPF); in the second phase Kirschen’s andBialek’s tracing algorithms are used and compared to evaluate the impact of each resource in the net-work. Finally, the MW-mile method is used in the third phase of the proposed model. A distributionnetwork of 33 buses with large penetration of DER is used to illustrate the application of the proposedmodel.