Smart City-Regional Governance: A 'Dual Transition'

This paper introduces the concept of ‘dual transition’ in relation to the notion of ‘smartness’ in city-regional governance. This consists of two intersecting dimensions, a broader change in political-economic and societal circumstances which surround city-regions, and a more detailed, local change towards ‘smartness’ in policy practices. Although ‘smart’ seems to have become a somewhat ubiquitous adjective in urban policy, it seeks to project policies that seek to go beyond a one-dimensional ‘growth agenda’ by addressing also the multifaceted quest for social, economic and environmental sustainability. The underlying shift in discourse, rationality and suggested policy responses in both dimensions may be captured by the concept of ‘transition’ in relation to policies and governance. Originally developed in conjunction with broad political-economic regime change, such as post-authoritarian democratisation, the idea of ‘transition’ has also been applied to the more specific concept of sustainability as ‘sustainability transition’.

Grid structure impact in sparse point representation of derivatives

In the Sparse Point Representation (SPR) method the principle is to retain the function data indicated by significant interpolatory wavelet coefficients, which are defined as interpolation errors by means of an interpolating subdivision scheme. Typically, a SPR grid is coarse in smooth regions, and refined close to irregularities. Furthermore, the computation of partial derivatives of a function from the information of its SPR content is performed in two steps. The first one is a refinement procedure to extend the SPR by the inclusion of new interpolated point values in a security zone. Then, for points in the refined grid, such derivatives are approximated by uniform finite differences, using a step size proportional to each point local scale. If required neighboring stencils are not present in the grid, the corresponding missing point values are approximated from coarser scales using the interpolating subdivision scheme. Using the cubic interpolation subdivision scheme, we demonstrate that such adaptive finite differences can be formulated in terms of a collocation scheme based on the wavelet expansion associated to the SPR. For this purpose, we prove some results concerning the local behavior of such wavelet reconstruction operators, which stand for SPR grids having appropriate structures. This statement implies that the adaptive finite difference scheme and the one using the step size of the finest level produce the same result at SPR grid points. Consequently, in addition to the refinement strategy, our analysis indicates that some care must be taken concerning the grid structure, in order to keep the truncation error under a certain accuracy limit. Illustrating results are presented for 2D Maxwell's equation numerical solutions.

Data mining techniques contributions to support electrical vehicle demand response

The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.

Electric vehicles in urban areas: test cases using a scenario simulator

Electric vehicles introduction will affect cities environment and urban mobility policies. Network system operators will have to consider the electric vehicles in planning and operation activities due to electric vehicles’ dependency on the electricity grid. The present paper presents test cases using an Electric Vehicle Scenario Simulator (EVeSSi) being developed by the authors. The test cases include two scenarios considering a 33 bus network with up to 2000 electric vehicles in the urban area. The scenarios consider a penetration of 10% of electric vehicles (200 of 2000), 30% (600) and 100% (2000). The first scenario will evaluate network impacts and the second scenario will evaluate CO2 emissions and fuel consumption.

Energy efficiency analysis considering the use of an intelligent systems management in a smart home

In this abstract is presented an energy management system included in a SCADA system existent in a intelligent home. The system control the home energy resources according to the players definitions (electricity consumption and comfort levels), the electricity prices variation in real time mode and the DR events proposed by the aggregators.

Using data mining techniques to support DR programs definition in smart grids

In recent years, Power Systems (PS) have experimented many changes in their operation. The introduction of new players managing Distributed Generation (DG) units, and the existence of new Demand Response (DR) programs make the control of the system a more complex problem and allow a more flexible management. An intelligent resource management in the context of smart grids is of huge important so that smart grids functions are assured. This paper proposes a new methodology to support system operators and/or Virtual Power Players (VPPs) to determine effective and efficient DR programs that can be put into practice. This method is based on the use of data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 32 bus distribution network.

VPP´s multi-level negotiation in smart grids and competitive electricity markets

The increase of distributed generation (DG) has brought about new challenges in electrical networks electricity markets and in DG units operation and management. Several approaches are being developed to manage the emerging potential of DG, such as Virtual Power Players (VPPs), which aggregate DG plants; and Smart Grids, an approach that views generation and associated loads as a subsystem. This paper presents a multi-level negotiation mechanism for Smart Grids optimal operation and negotiation in the electricity markets, considering the advantages of VPPs’ management. The proposed methodology is implemented and tested in MASCEM – a multiagent electricity market simulator, developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations.

ANN based support for distributed energy resources scheduling in smart grids

The future scenarios for operation of smart grids are likely to include a large diversity of players, of different types and sizes. With control and decision making being decentralized over the network, intelligence should also be decentralized so that every player is able to play in the market environment. In the new context, aggregator players, enabling medium, small, and even micro size players to act in a competitive environment, will be very relevant. Virtual Power Players (VPP) and single players must optimize their energy resource management in order to accomplish their goals. This is relatively easy to larger players, with financial means to have access to adequate decision support tools, to support decision making concerning their optimal resource schedule. However, the smaller players have difficulties in accessing this kind of tools. So, it is required that these smaller players can be offered alternative methods to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), intended to support smaller players’ resource scheduling. The used methodology uses a training set that is built using the energy resource scheduling solutions obtained with a reference optimization methodology, a mixed-integer non-linear programming (MINLP) in this case. The trained network is able to achieve good schedule results requiring modest computational means.

Intelligent energy resources management in the context of smart grids

Smart grids are envisaged as infrastructures able to accommodate all centralized and distributed energy resources (DER), including intensive use of renewable and distributed generation (DG), storage, demand response (DR), and also electric vehicles (EV), from which plug-in vehicles, i.e. gridable vehicles, are especially relevant. Moreover, smart grids must accommodate a large number of diverse types or players in the context of a competitive business environment. Smart grids should also provide the required means to efficiently manage all these resources what is especially important in order to make the better possible use of renewable based power generation, namely to minimize wind curtailment. An integrated approach, considering all the available energy resources, including demand response and storage, is crucial to attain these goals. This paper proposes a methodology for energy resource management that considers several Virtual Power Players (VPPs) managing a network with high penetration of distributed generation, demand response, storage units and network reconfiguration. The resources are controlled through a flexible SCADA (Supervisory Control And Data Acquisition) system that can be accessed by the evolved entities (VPPs) under contracted use conditions. A case study evidences the advantages of the proposed methodology to support a Virtual Power Player (VPP) managing the energy resources that it can access in an incident situation.

Multilevel negotiation in smart grids for VPP management of distributed resources

A multilevel negotiation mechanism for operating smart grids and negotiating in electricity markets considers the advantages of virtual power player management.

Scheduling distributed energy resources in an isolated grid - an artificial neural network approach

Power Systems (PS), have been affected by substantial penetration of Distributed Generation (DG) and the operation in competitive environments. The future PS will have to deal with large-scale integration of DG and other distributed energy resources (DER), such as storage means, and provide to market agents the means to ensure a flexible and secure operation. Virtual power players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. This paper proposes an artificial neural network (ANN) based methodology to support VPP resource schedule. The trained network is able to achieve good schedule results requiring modest computational means. A real data test case is presented.

Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming

In the energy management of the isolated operation of small power system, the economic scheduling of the generation units is a crucial problem. Applying right timing can maximize the performance of the supply. The optimal operation of a wind turbine, a solar unit, a fuel cell and a storage battery is searched by a mixed-integer linear programming implemented in General Algebraic Modeling Systems (GAMS). A Virtual Power Producer (VPP) can optimal operate the generation units, assured the good functioning of equipment, including the maintenance, operation cost and the generation measurement and control. A central control at system allows a VPP to manage the optimal generation and their load control. The application of methodology to a real case study in Budapest Tech, demonstrates the effectiveness of this method to solve the optimal isolated dispatch of the DC micro-grid renewable energy park. The problem has been converged in 0.09 s and 30 iterations.

Análise do produtor-consumidor enquanto interveniente da rede elétrica inteligente

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia

Smart environment technology as a possible enabler of smart cities

This paper discusses the technology of smart floors as a enabler of smart cities. The discussion will be based on technology that is embedded into the environment that enable location, navigation but also wireless power transmission for powering up elements siting on it, typically mobile devices. One of those examples is the smart floor, this implementation follows two paths, one where the floor is passive, and normally passive RFID's are embedded into the floor, they are used to provide intelligence into the surrounding space, this is normally complemented with a battery powered mobile unit that scans the floor for the sensors and communicates the information to a database which locates the mobile device in the environment. The other path for the smart city enabler is where the floor is active and delivers energy for the objects standing on top of it. In this paper these two approaches will be presented, by discussing the technology behind it. © 2014 IEEE.

Smart object for 3D interaction

This paper reports on the creation of an interface for 3D virtual environments, computer-aided design applications or computer games. Standard computer interfaces are bound to 2D surfaces, e.g., computer mouses, keyboards, touch pads or touch screens. The Smart Object is intended to provide the user with a 3D interface by using sensors that register movement (inertial measurement unit), touch (touch screen) and voice (microphone). The design and development process as well as the tests and results are presented in this paper. The Smart Object was developed by a team of four third-year engineering students from diverse scientific backgrounds and nationalities during one semester.