Advanced network reconfiguration system applied to CEMIG-D system

In the network reconfiguration context, the challenge nowadays is to improve the system in order to get intelligent systems that are able to monitor the network and produce refined information to support the operator decisions in real time, this because the network is wide, ramified and in some places difficult to access. The objective of this paper is to present the first results of the network reconfiguration algorithm that has been developed to CEMIG-D. The algorithm's main idea is to provide a new network configuration, after an event (fault or study case), based on an initial condition and aiming to minimize the affected load, considering the restrictions of load flow equations, maximum capacity of the lines as well as equipments and substations, voltage limits and system radial operation. Initial tests were made considering real data from the system, provided by CEMIG-D and it reveals very promising results. © 2013 IEEE.

Fundraising and investor relations in social enterprise: Marketing your way out of the pioneer gap in African off-grid renewable energy

Social enterprises apply the best of business for the pursuit of social or environmental mission while also generating revenues. Globally, nearly 1,3 billion people lack access to electricity, as well as another billion having access to only low quality and infrequent electricity. Off-grid renewable energy, like solar, will increasingly have a key role in the solution of the energy access issue. The pioneer gap in off-grid renewable energy consists of financing (or funding) gaps and capacity gaps, to do with both the early stage of the enterprises in question, as well as the early stage of the whole industry. The gaps are emphasised by specific characteristics of off-grid renewable energy business models and the requirements of operating in bottom-of-the-pyramid markets. The marketing perspective to fundraising is chosen to uncover the possible role enterprises themselves have in bridging the pioneer gap. The purpose of this thesis is to study how social enterprises operating in off-grid renewable energy in Africa utilise marketing activities in their investor relations in bridging the pioneer gap. This main research question is divided into the following sub-questions:  How does the pioneer gap affect fundraising for these enterprises?  How are the funding needs for these enterprises characterised?  How do these enterprises build trust in their investor relations? The theoretic framework is built on relationship marketing and investor relations, with an emphasis on creation of trust. The research is conducted as a thematical case study. Primary data is gathered via semi-structured interviews with six solar energy companies and two accelerators. According to the findings, the main components affecting trust-creation are diminished information asymmetry and perceived risk, mission alignment as well as a personal fit or relationship with the investor. Therefore, an enterprise can utilise e.g. the following marketing activities in their investor relations to bridge the pioneer gap: ensuring investor material, the enterprise story and presenting of them is clear, concise and complete to “package” the enterprise as an investment; taking investor needs and motivations into account as well as utilising existing investors as ambassadors.

Power imbalance application region method for distributed synchronous generator anti-islanding protection design and evaluation

This paper presents a novel graphical approach to adjust and evaluate frequency-based relays employed in anti-islanding protection schemes of distributed synchronous generators, in order to meet the anti-islanding and abnormal frequency variation requirements, simultaneously. The proposed method defines a region in the power mismatch space, inside which the relay non-detection zone should be located, if the above-mentioned requirements must be met. Such region is called power imbalance application region. Results show that this method can help protection engineers to adjust frequency-based relays to improve the anti-islanding capability and to minimize false operation occurrences, keeping the abnormal frequency variation utility requirements satisfied. Moreover, the proposed method can be employed to coordinate different types of frequency-based relays, aiming at improving overall performance of the distributed generator frequency protection scheme. (C) 2011 Elsevier B.V. All rights reserved.

An integrated approach for distributed energy resource short term scheduling in smart grids considering realistic power system simulation

The large increase of distributed energy resources, including distributed generation, storage systems and demand response, especially in distribution networks, makes the management of the available resources a more complex and crucial process. With wind based generation gaining relevance, in terms of the generation mix, the fact that wind forecasting accuracy rapidly drops with the increase of the forecast anticipation time requires to undertake short-term and very short-term re-scheduling so the final implemented solution enables the lowest possible operation costs. This paper proposes a methodology for energy resource scheduling in smart grids, considering day ahead, hour ahead and five minutes ahead scheduling. The short-term scheduling, undertaken five minutes ahead, takes advantage of the high accuracy of the very-short term wind forecasting providing the user with more efficient scheduling solutions. The proposed method uses a Genetic Algorithm based approach for optimization that is able to cope with the hard execution time constraint of short-term scheduling. Realistic power system simulation, based on PSCAD , is used to validate the obtained solutions. The paper includes a case study with a 33 bus distribution network with high penetration of distributed energy resources implemented in PSCAD .

Hybrid Power System Intelligent Operation and Protection Involving Plug-in Electric Vehicles

Two key solutions to reduce the greenhouse gas emissions and increase the overall energy efficiency are to maximize the utilization of renewable energy resources (RERs) to generate energy for load consumption and to shift to low or zero emission plug-in electric vehicles (PEVs) for transportation. The present U.S. aging and overburdened power grid infrastructure is under a tremendous pressure to handle the issues involved in penetration of RERS and PEVs. The future power grid should be designed with for the effective utilization of distributed RERs and distributed generations to intelligently respond to varying customer demand including PEVs with high level of security, stability and reliability. This dissertation develops and verifies such a hybrid AC-DC power system. The system will operate in a distributed manner incorporating multiple components in both AC and DC styles and work in both grid-connected and islanding modes. The verification was performed on a laboratory-based hybrid AC-DC power system testbed as hardware/software platform. In this system, RERs emulators together with their maximum power point tracking technology and power electronics converters were designed to test different energy harvesting algorithms. The Energy storage devices including lithium-ion batteries and ultra-capacitors were used to optimize the performance of the hybrid power system. A lithium-ion battery smart energy management system with thermal and state of charge self-balancing was proposed to protect the energy storage system. A grid connected DC PEVs parking garage emulator, with five lithium-ion batteries was also designed with the smart charging functions that can emulate the future vehicle-to-grid (V2G), vehicle-to-vehicle (V2V) and vehicle-to-house (V2H) services. This includes grid voltage and frequency regulations, spinning reserves, micro grid islanding detection and energy resource support. The results show successful integration of the developed techniques for control and energy management of future hybrid AC-DC power systems with high penetration of RERs and PEVs.

Hybrid power system intelligent operation and protection involving plug-in electric vehicles

Two key solutions to reduce the greenhouse gas emissions and increase the overall energy efficiency are to maximize the utilization of renewable energy resources (RERs) to generate energy for load consumption and to shift to low or zero emission plug-in electric vehicles (PEVs) for transportation. The present U.S. aging and overburdened power grid infrastructure is under a tremendous pressure to handle the issues involved in penetration of RERS and PEVs. The future power grid should be designed with for the effective utilization of distributed RERs and distributed generations to intelligently respond to varying customer demand including PEVs with high level of security, stability and reliability. This dissertation develops and verifies such a hybrid AC-DC power system. The system will operate in a distributed manner incorporating multiple components in both AC and DC styles and work in both grid-connected and islanding modes. ^ The verification was performed on a laboratory-based hybrid AC-DC power system testbed as hardware/software platform. In this system, RERs emulators together with their maximum power point tracking technology and power electronics converters were designed to test different energy harvesting algorithms. The Energy storage devices including lithium-ion batteries and ultra-capacitors were used to optimize the performance of the hybrid power system. A lithium-ion battery smart energy management system with thermal and state of charge self-balancing was proposed to protect the energy storage system. A grid connected DC PEVs parking garage emulator, with five lithium-ion batteries was also designed with the smart charging functions that can emulate the future vehicle-to-grid (V2G), vehicle-to-vehicle (V2V) and vehicle-to-house (V2H) services. This includes grid voltage and frequency regulations, spinning reserves, micro grid islanding detection and energy resource support. ^ The results show successful integration of the developed techniques for control and energy management of future hybrid AC-DC power systems with high penetration of RERs and PEVs.^

Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints

Energy resource scheduling becomes increasingly important, as the use of distributed resources is intensified and massive gridable vehicle use is envisaged. The present paper proposes a methodology for dayahead energy resource scheduling for smart grids considering the intensive use of distributed generation and of gridable vehicles, usually referred as Vehicle- o-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with V2G owners. It takes into account these contracts, the user´s requirements subjected to the VPP, and several discharge price steps. Full AC power flow calculation included in the model allows taking into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33 bus distribution network and V2G is used to illustrate the good performance of the proposed method.

LMP based bid formation for virtual power players operating in smart grids

Power system organization has gone through huge changes in the recent years. Significant increase in distributed generation (DG) and operation in the scope of liberalized markets are two relevant driving forces for these changes. More recently, the smart grid (SG) concept gained increased importance, and is being seen as a paradigm able to support power system requirements for the future. This paper proposes a computational architecture to support day-ahead Virtual Power Player (VPP) bid formation in the smart grid context. This architecture includes a forecasting module, a resource optimization and Locational Marginal Price (LMP) computation module, and a bid formation module. Due to the involved problems characteristics, the implementation of this architecture requires the use of Artificial Intelligence (AI) techniques. Artificial Neural Networks (ANN) are used for resource and load forecasting and Evolutionary Particle Swarm Optimization (EPSO) is used for energy resource scheduling. The paper presents a case study that considers a 33 bus distribution network that includes 67 distributed generators, 32 loads and 9 storage units.

Distributed energy resources management with cyber-physical SCADA in the context of future smart grids

In the energy management of a small power system, the scheduling of the generation units is a crucial problem for which adequate methodologies can maximize the performance of the energy supply. This paper proposes an innovative methodology for distributed energy resources management. The optimal operation of distributed generation, demand response and storage resources is formulated as a mixed-integer linear programming model (MILP) and solved by a deterministic optimization technique CPLEX-based implemented in General Algebraic Modeling Systems (GAMS). The paper deals with a vision for the grids of the future, focusing on conceptual and operational aspects of electrical grids characterized by an intensive penetration of DG, in the scope of competitive environments and using artificial intelligence methodologies to attain the envisaged goals. These concepts are implemented in a computational framework which includes both grid and market simulation.

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.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Pricing of reactive power support provided by distributed generators in transmission systems

Distributed Generation, microgrid technologies, two-way communication systems, and demand response programs are issues that are being studied in recent years within the concept of smart grids. At some level of enough penetration, the Distributed Generators (DGs) can provide benefits for sub-transmission and transmission systems through the so-called ancillary services. This work is focused on the ancillary service of reactive power support provided by DGs, specifically Wind Turbine Generators (WTGs), with high level of impact on transmission systems. The main objective of this work is to propose an optimization methodology to price this service by determining the costs in which a DG incurs when it loses sales opportunity of active power, i.e, by determining the Loss of Opportunity Costs (LOC). LOC occur when more reactive power is required than available, and the active power generation has to be reduced in order to increase the reactive power capacity. In the optimization process, three objectives are considered: active power generation costs of DGs, voltage stability margin of the system, and losses in the lines of the network. Uncertainties of WTGs are reduced solving multi-objective optimal power flows in multiple probabilistic scenarios constructed by Monte Carlo simulations, and modeling the time series associated with the active power generation of each WTG via Fuzzy Logic and Markov Chains. The proposed methodology was tested using the IEEE 14 bus test system with two WTGs installed. © 2011 IEEE.

Improving the grid operation and reliability cost of distribution systems with dispersed generation

In this work, a mathematical model to analyze the impact of the installation and operation of dispersed generation units in power distribution systems is proposed. The main focus is to determine the trade-off between the reliability and operational costs of distribution networks when the operation of isolated areas is allowed. In order to increase the system operator revenue, an optimal power flow makes use of the different energy prices offered by the dispersed generation connected to the grid. Simultaneously, the type and location of the protective devices initially installed on the protection system are reconfigured in order to minimize the interruption and expenditure of adjusting the protection system to conditions imposed by the operation of dispersed units. The interruption cost regards the unsupplied energy to customers in secure systems but affected by the normal tripping of protective devices. Therefore, the tripping of fuses, reclosers, and overcurrent relays aims to protect the system against both temporary and permanent fault types. Additionally, in order to reduce the average duration of the system interruption experienced by customers, the isolated operation of dispersed generation is allowed by installing directional overcurrent relays with synchronized reclose capabilities. A 135-bus real distribution system is used in order to show the advantages of using the mathematical model proposed. © 1969-2012 IEEE.

Active power reserve for frequency control provided by distributed generators in distribution networks

In a smart grid environment, attention should be paid not only to the power supplied to satisfy loads and system losses but also to the services necessary to provide security and stability to the system: the so-called ancillary services. As they are well known the benefits that distributed generation can bring to electrical systems and to the environment, in this work the possibility that active power reserve for frequency control could be provided by distributed generators (DGs) in an efficient and economical way is explored. The proposed methodology was tested using the IEEE 34-bus distribution test system. The results show improvements in the capacity of the system for this ancillary service and decrease in system losses and payments of the distribution system operator to the DGs.

Multifunctional current reference generation strategy for grid-tied power electronic converter

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)