Short-term scheduling considering five-minute and hour-ahead energy resource management

The introduction of new distributed energy resources, based on natural intermittent power sources, in power systems imposes the development of new adequate operation management and control methods. This paper proposes a short-term Energy Resource Management (ERM) methodology performed in two phases. The first one addresses the hour-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. Both phases consider the day-ahead resource scheduling solution. The ERM scheduling is formulated as an optimization problem that aims to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixed-integer non-linear programming approach and by a heuristic approach based on genetic algorithms. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units has been implemented in a PSCADbased simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

Residential energy resources management by a SCADA system in case of excess wind generation

The end consumers in a smart grid context are seen as active players. The distributed generation resources applied in smart home system as a micro and small-scale systems can be wind generation, photovoltaic and combine heat and power facility. The paper addresses the management of domestic consumer resources, i.e. wind generation, solar photovoltaic, combined heat and power, electric vehicle with gridable capability and loads, in a SCADA system with intelligent methodology to support the user decision in real time. The main goal is to obtain the better management of excess wind generation that may arise in consumer’s distributed generation resources. The optimization methodology is performed in a SCADA House Intelligent Management context and the results are analyzed to validate the SCADA system.

Intelligent electric vehicle heuristic for energy resource management using simulated annealing

The smart grid concept appears as a suitable solution to guarantee the power system operation in the new electricity paradigm with electricity markets and integration of large amounts of Distributed Energy Resources (DERs). Virtual Power Player (VPP) will have a significant importance in the management of a smart grid. In the context of this new paradigm, Electric Vehicles (EVs) rise as a good available resource to be used as a DER by a VPP. This paper presents the application of the Simulated Annealing (SA) technique to solve the Energy Resource Management (ERM) of a VPP. It is also presented a new heuristic approach to intelligently handle the charge and discharge of the EVs. This heuristic process is incorporated in the SA technique, in order to improve the results of the ERM. The case study shows the results of the ERM for a 33-bus distribution network with three different EVs penetration levels, i. e., with 1000, 2000 and 3000 EVs. The results of the proposed adaptation of the SA technique are compared with a previous SA version and a deterministic technique.

Intelligent energy resource management considering vehicle-to-grid: A simulated annealing approach

This paper proposes a simulated annealing (SA) approach to address energy resources management from the point of view of a virtual power player (VPP) operating in a smart grid. Distributed generation, demand response, and gridable vehicles are intelligently managed on a multiperiod basis according to V2G user´s profiles and requirements. Apart from using the aggregated resources, the VPP can also purchase additional energy from a set of external suppliers. The paper includes a case study for a 33 bus distribution network with 66 generators, 32 loads, and 1000 gridable vehicles. The results of the SA approach are compared with a methodology based on mixed-integer nonlinear programming. A variation of this method, using ac load flow, is also used and the results are compared with the SA solution using network simulation. The proposed SA approach proved to be able to obtain good solutions in low execution times, providing VPPs with suitable decision support for the management of a large number of distributed resources.

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.

Intelligent multi-player smart grid management considering distributed energy resources and demand response

The smart grid concept is rapidly evolving in the direction of practical implementations able to bring smart grid advantages into practice. Evolution in legacy equipment and infrastructures is not sufficient to accomplish the smart grid goals as it does not consider the needs of the players operating in a complex environment which is dynamic and competitive in nature. Artificial intelligence based applications can provide solutions to these problems, supporting decentralized intelligence and decision-making. A case study illustrates the importance of Virtual Power Players (VPP) and multi-player negotiation in the context of smart grids. This case study is based on real data and aims at optimizing energy resource management, considering generation, storage and demand response.

Energy resources management for more sustainable distribution systems: an intelligent approach

Energy Resources Management can play a very relevant role in future power systems in SmartGrid context, with high penetration of distributed generation and storage systems. This paper deals with the importance of resources management in incident situation. The system to consider a high penetration of distributed generation, demand response, storage units and network reconfiguration. A case study evidences the advantages of using a flexible SCADA to control the energy resources in incident situation.

Emergency energy resource management in SmartGrids

Energy resources management can play a very relevant role in future power systems in a SmartGrid context, with intensive penetration of distributed generation and storage systems. This paper deals with the importance of resource management in incident situations. The paper presents DemSi, an energy resources management simulator that has been developed by the authors to simulate electrical distribution networks with high distributed generation penetration, storage in network points and customers with demand response contracts. DemSi is used to undertake simulations for an incident scenario, evidencing the advantages of adequately using flexible contracts, storage, and reserve in order to limit incident consequences.

Energy resource management in smart grids considering an Intensive use of electric vehicles

The introduction of electricity markets and integration of Distributed Generation (DG) have been influencing the power system’s structure change. Recently, the smart grid concept has been introduced, to guarantee a more efficient operation of the power system using the advantages of this new paradigm. Basically, a smart grid is a structure that integrates different players, considering constant communication between them to improve power system operation and management. One of the players revealing a big importance in this context is the Virtual Power Player (VPP). In the transportation sector the Electric Vehicle (EV) is arising as an alternative to conventional vehicles propel by fossil fuels. The power system can benefit from this massive introduction of EVs, taking advantage on EVs’ ability to connect to the electric network to charge, and on the future expectation of EVs ability to discharge to the network using the Vehicle-to-Grid (V2G) capacity. This thesis proposes alternative strategies to control these two EV modes with the objective of enhancing the management of the power system. Moreover, power system must ensure the trips of EVs that will be connected to the electric network. The EV user specifies a certain amount of energy that will be necessary to charge, in order to ensure the distance to travel. The introduction of EVs in the power system turns the Energy Resource Management (ERM) under a smart grid environment, into a complex problem that can take several minutes or hours to reach the optimal solution. Adequate optimization techniques are required to accommodate this kind of complexity while solving the ERM problem in a reasonable execution time. This thesis presents a tool that solves the ERM considering the intensive use of EVs in the smart grid context. The objective is to obtain the minimum cost of ERM considering: the operation cost of DG, the cost of the energy acquired to external suppliers, the EV users payments and remuneration and penalty costs. This tool is directed to VPPs that manage specific network areas, where a high penetration level of EVs is expected to be connected in these areas. The ERM is solved using two methodologies: the adaptation of a deterministic technique proposed in a previous work, and the adaptation of the Simulated Annealing (SA) technique. With the purpose of improving the SA performance for this case, three heuristics are additionally proposed, taking advantage on the particularities and specificities of an ERM with these characteristics. A set of case studies are presented in this thesis, considering a 32 bus distribution network and up to 3000 EVs. The first case study solves the scheduling without considering EVs, to be used as a reference case for comparisons with the proposed approaches. The second case study evaluates the complexity of the ERM with the integration of EVs. The third case study evaluates the performance of scheduling with different control modes for EVs. These control modes, combined with the proposed SA approach and with the developed heuristics, aim at improving the quality of the ERM, while reducing drastically its execution time. The proposed control modes are: uncoordinated charging, smart charging and V2G capability. The fourth and final case study presents the ERM approach applied to consecutive days.

Distribution system operation supported by contextual energy resource management based on intelligent SCADA

Future distribution systems will have to deal with an intensive penetration of distributed energy resources ensuring reliable and secure operation according to the smart grid paradigm. SCADA (Supervisory Control and Data Acquisition) is an essential infrastructure for this evolution. This paper proposes a new conceptual design of an intelligent SCADA with a decentralized, flexible, and intelligent approach, adaptive to the context (context awareness). This SCADA model is used to support the energy resource management undertaken by a distribution network operator (DNO). Resource management considers all the involved costs, power flows, and electricity prices, allowing the use of network reconfiguration and load curtailment. Locational Marginal Prices (LMP) are evaluated and used in specific situations to apply Demand Response (DR) programs on a global or a local basis. The paper includes a case study using a 114 bus distribution network and load demand based on real data.

A Hybrid Simulated Annealing approach to handle Energy Resource Management considering an intensive use of Electric Vehicles

The massification of electric vehicles (EVs) can have a significant impact on the power system, requiring a new approach for the energy resource management. The energy resource management has the objective to obtain the optimal scheduling of the available resources considering distributed generators, storage units, demand response and EVs. The large number of resources causes more complexity in the energy resource management, taking several hours to reach the optimal solution which requires a quick solution for the next day. Therefore, it is necessary to use adequate optimization techniques to determine the best solution in a reasonable amount of time. This paper presents a hybrid artificial intelligence technique to solve a complex energy resource management problem with a large number of resources, including EVs, connected to the electric network. The hybrid approach combines simulated annealing (SA) and ant colony optimization (ACO) techniques. The case study concerns different EVs penetration levels. Comparisons with a previous SA approach and a deterministic technique are also presented. For 2000 EVs scenario, the proposed hybrid approach found a solution better than the previous SA version, resulting in a cost reduction of 1.94%. For this scenario, the proposed approach is approximately 94 times faster than the deterministic approach.

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.

Simulated Annealing Approach Applied to the Energy Resource Management Considering Demand Response for Electric Vehicle

The aggregation and management of Distributed Energy Resources (DERs) by an Virtual Power Players (VPP) is an important task in a smart grid context. The Energy Resource Management (ERM) of theses DERs can become a hard and complex optimization problem. The large integration of several DERs, including Electric Vehicles (EVs), may lead to a scenario in which the VPP needs several hours to have a solution for the ERM problem. This is the reason why it is necessary to use metaheuristic methodologies to come up with a good solution with a reasonable amount of time. The presented paper proposes a Simulated Annealing (SA) approach to determine the ERM considering an intensive use of DERs, mainly EVs. In this paper, the possibility to apply Demand Response (DR) programs to the EVs is considered. Moreover, a trip reduce DR program is implemented. The SA methodology is tested on a 32-bus distribution network with 2000 EVs, and the SA results are compared with a deterministic technique and particle swarm optimization results.