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Special Issue "Decentralized Management of Energy Streams in Smart Grids"

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (30 April 2016)

Special Issue Editor

Guest Editor
Prof. Dr. G.J.M. (Gerard) Smit

Department of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, PO Box 217, 7500 AE, Enschede, Netherlands
Website | E-Mail
Interests: Demand Side Management, Smart Grids; energy-autonomous regions; distributed optimization algorithms; embedded systems; low-power systems; control of storage systems; local heat distribution systems

Special Issue Information

Dear Collegaues,

Many countries have a political target to become less dependent on fossil fuels. As a consequence, more and more energy is generated from fluctuating renewable energy sources (e.g., PV, wind, biogas, hydro, geothermic). The continuous growth of renewable generation, accompanied by their decentralized operation, is leading to massive grid investment needs if no appropriate actions are taken. With the current grid infrastructure, and an increasing percentage of renewable energy generation, there will be days that, during certain hours (e.g., around noon with low PV and wind production), not all renewable energy generated in certain parts of the power grid can be transported to other regions and, therefore, has to be curtailed. On the other hand, it is also expected that the need for electricity will grow in the future due to an increasing electrification of heating and transport. Large quantities of E-vehicles and heat pumps enlarge variability and lead to higher peak load concentrations, which may increase the need for costly grid capacity investments.

To avoid or reduce the need for grid investments, especially in distribution grids, it is essential to exploit the flexibility available in the grid, e.g., by controlling/optimizing the charging of E-vehicles, time-shiftable appliances (e.g., washing machines, air-conditioners, freezers, heat pumps). and storage assets. Such an optimization of energy streams is often called Demand Side Management (DSM) and has the goal to reach a certain objective for the consumption of electricity within a distribution grid. The objective may for example be market driven or technology driven (e.g., avoiding violation of grid restrictions).

In a longer term, with a much higher penetration of renewable generation, it will become even more important to optimize the power profiles of parts of the power grid, not only within a day, but also over days or weeks, since, otherwise, the resulting imbalances will ask for a large amount of central reserve capacity (predominantly based on fossil fuels and dimensioned for the largest peak and hence operating with a low efficiency). For this, different forms of (decentralized) energy storage assets for short-term, as well as long-term, storage are needed. These storage assets need to be controlled/optimized as well.

In this Special Issue of Energies, we ask for original contributions related to decentralized management of energy streams in Smart Grids.

Prof. Dr. G.J.M. (Gerard) Smit
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Decentralized energy management
  • Demand Side Management
  • Control of Storage in Smart Grids
  • Optimization algorithms for Smart Grids
  • Microgrids
  • Control algorithms for charging of E-vehicles
  • Control of infeed of renewables
  • Power quality in distribution grids
  • Prediction algorithms of renewables

Published Papers (18 papers)

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Open AccessArticle Increasing the Benefit from Cost-Minimizing Loads via Centralized Adjustments
Energies 2016, 9(12), 983; https://doi.org/10.3390/en9120983
Received: 9 September 2016 / Revised: 1 November 2016 / Accepted: 17 November 2016 / Published: 25 November 2016
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Abstract
Several demand response (DR) strategies rely on real-time pricing and selfish local optimization, which may not result in optimal electricity consumption patterns from the viewpoint of an energy supplier or a power system. Thus, this paper proposes a strategy enabling centralized adjustments to
[...] Read more.
Several demand response (DR) strategies rely on real-time pricing and selfish local optimization, which may not result in optimal electricity consumption patterns from the viewpoint of an energy supplier or a power system. Thus, this paper proposes a strategy enabling centralized adjustments to cost-minimize consumers’ load. By employing the strategy, an aggregator is able to alter electricity consumption in order to remove power imbalances and to participate in the balancing power market (BPM). In this paper, we focus on direct electric space heating (DESH) loads that aim to minimize their heating cost locally. The consumers and an aggregator agree about an indoor temperature band, within which the aggregator is allowed to alter the temperature, and thus the electricity consumption. Centrally, the aggregator procures its electricity demand from a day-ahead (DA) market by utilizing the allowed temperature band and employs the band later in real-time (RT) operation for the balancing of its own imbalances or regulating power in the BPM. Full article
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
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Open AccessArticle Does Your Domestic Photovoltaic Energy System Survive Grid Outages?
Energies 2016, 9(9), 736; https://doi.org/10.3390/en9090736
Received: 30 April 2016 / Revised: 30 August 2016 / Accepted: 31 August 2016 / Published: 9 September 2016
PDF Full-text (847 KB) | HTML Full-text | XML Full-text
Abstract
Domestic renewable energy systems, including photovoltaic energy generation, as well as local storage, are becoming increasingly popular and economically feasible, but do come with a wide range of options. Hence, it can be difficult to match their specification to specific customer’s needs. Next
[...] Read more.
Domestic renewable energy systems, including photovoltaic energy generation, as well as local storage, are becoming increasingly popular and economically feasible, but do come with a wide range of options. Hence, it can be difficult to match their specification to specific customer’s needs. Next to the usage-specific demand profiles and location-specific production profiles, local energy storage through the use of batteries is becoming increasingly important, since it allows one to balance variations in production and demand, either locally or via the grid. Moreover, local storage can also help to ensure a continuous energy supply in the presence of grid outages, at least for a while. Hybrid Petri net (HPN) models allow one to analyze the effect of different battery management strategies on the continuity of such energy systems in the case of grid outages. The current paper focuses on one of these strategies, the so-called smart strategy, that reserves a certain percentage of the battery capacity to be only used in case of grid outages. Additionally, we introduce a new strategy that makes better use of the reserved backup capacity, by reducing the demand in the presence of a grid outage through a prioritization mechanism. This new strategy, called power-save, only allows the essential (high-priority) demand to draw from the battery during power outages. We show that this new strategy outperforms previously-proposed strategies through a careful analysis of a number of scenarios and for a selection of survivability measures, such as minimum survivability per day, number of survivable hours per day, minimum survivability per year and various survivability quantiles. Full article
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
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Open AccessArticle A Coordinated Control for Photovoltaic Generators and Energy Storages in Low-Voltage AC/DC Hybrid Microgrids under Islanded Mode
Energies 2016, 9(8), 651; https://doi.org/10.3390/en9080651
Received: 30 May 2016 / Revised: 9 August 2016 / Accepted: 10 August 2016 / Published: 17 August 2016
Cited by 8 | PDF Full-text (3984 KB) | HTML Full-text | XML Full-text
Abstract
The increasing penetration of renewable generators can be a significant challenge due to the fluctuation of their power generation. Energy storage (ES) units are one solution to improve power supply quality and guarantee system stability. In this paper, a hybrid microgrid is built
[...] Read more.
The increasing penetration of renewable generators can be a significant challenge due to the fluctuation of their power generation. Energy storage (ES) units are one solution to improve power supply quality and guarantee system stability. In this paper, a hybrid microgrid is built based on photovoltaic (PV) generator and ES; and coordinated control is proposed and developed to achieve power management in a decentralized manner. This control scheme contains three different droop strategies according to characteristics of PV and ES. First, the modified droop control is proposed for PV, which can take full utilization of renewable energy and avoid regulating output active power frequently. Second, to maintain the direct current (DC) bus voltage stability, a novel droop control incorporating a constant power band is presented for DC-side ES. Third, a cascade droop control is designed for alternating current (AC)-side ES. Thus, the ES lifetime is prolonged. Moreover, interlinking converters (ICs) provide a bridge between AC/DC buses in a hybrid microgrid. The power control of IC is enabled when the AC- or DC-side suffer from active power demand shortage. In particular, if the AC microgrid does not satisfy the reactive power demand, IC then acts as a static synchronous compensator (STATCOM). The effectiveness of the proposed strategies is verified by simulations. Full article
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
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Open AccessArticle Robust Peak-Shaving for a Neighborhood with Electric Vehicles
Energies 2016, 9(8), 594; https://doi.org/10.3390/en9080594
Received: 4 May 2016 / Revised: 1 July 2016 / Accepted: 21 July 2016 / Published: 28 July 2016
Cited by 6 | PDF Full-text (307 KB) | HTML Full-text | XML Full-text
Abstract
Demand Side Management (DSM) is a popular approach for grid-aware peak-shaving. The most commonly used DSM methods either have no look ahead feature and risk deploying flexibility too early, or they plan ahead using predictions, which are in general not very reliable. To
[...] Read more.
Demand Side Management (DSM) is a popular approach for grid-aware peak-shaving. The most commonly used DSM methods either have no look ahead feature and risk deploying flexibility too early, or they plan ahead using predictions, which are in general not very reliable. To counter this, a DSM approach is presented that does not rely on detailed power predictions, but only uses a few easy to predict characteristics. By using these characteristics alone, near optimal results can be achieved for electric vehicle (EV) charging, and a bound on the maximal relative deviation is given. This result is extended to an algorithm that controls a group of EVs such that a transformer peak is avoided, while simultaneously keeping the individual house profiles as flat as possible to avoid cable overloading and for improved power quality. This approach is evaluated using different data sets to compare the results with the state-of-the-art research. The evaluation shows that the presented approach is capable of peak-shaving at the transformer level, while keeping the voltages well within legal bounds, keeping the cable load low and obtaining low losses. Further advantages of the methodology are a low communication overhead, low computational requirements and ease of implementation. Full article
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
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Open AccessArticle An Integer Linear Programming Model for an Ecovat Buffer
Energies 2016, 9(8), 592; https://doi.org/10.3390/en9080592
Received: 29 April 2016 / Revised: 1 July 2016 / Accepted: 19 July 2016 / Published: 28 July 2016
Cited by 1 | PDF Full-text (1389 KB) | HTML Full-text | XML Full-text
Abstract
An increase in the number of volatile renewables in the electricity grid enhances the imbalance of supply and demand. One promising candidate to solve this problem is to improve the energy storage. The Ecovat system is a new seasonal thermal energy storage system
[...] Read more.
An increase in the number of volatile renewables in the electricity grid enhances the imbalance of supply and demand. One promising candidate to solve this problem is to improve the energy storage. The Ecovat system is a new seasonal thermal energy storage system currently under development. In this paper, an integer linear programming model is developed to describe the behaviour and potential of this system. Furthermore, it is compared with a previously developed model, which is simplifying the behaviour of the Ecovat system much more, but is much less computationally expensive. It is shown that the new approach performs significantly better for several cases. For controlling a real Ecovat system in the future we may incorporate a number of improvements identified by our comparison analysis into the previously developed approach, which may help increase the quality of the obtained results without increasing the computational effort too much. Full article
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
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Open AccessArticle A Novel Strategy for Optimising Decentralised Energy Exchange for Prosumers
Energies 2016, 9(7), 554; https://doi.org/10.3390/en9070554
Received: 30 April 2016 / Revised: 1 July 2016 / Accepted: 12 July 2016 / Published: 18 July 2016
Cited by 7 | PDF Full-text (563 KB) | HTML Full-text | XML Full-text
Abstract
The realization of the Smart Grid vision will change the way of producing and distributing electrical energy. It paves the road for end-users to become pro-active in the distribution system and, equipped with renewable energy generators such as a photovoltaic panel, to become
[...] Read more.
The realization of the Smart Grid vision will change the way of producing and distributing electrical energy. It paves the road for end-users to become pro-active in the distribution system and, equipped with renewable energy generators such as a photovoltaic panel, to become a so called “prosumer”. The prosumer is engaged in both energy production and consumption. Prosumers’ energy can be transmitted and exchanged as a commodity between end-users, disrupting the traditional utility model. The appeal of such scenario lies in the engagement of the end user, in facilitating the introduction and optimization of renewables, and in engaging the end-user in its energy management. To facilitate the transition to a prosumers’ governed grid, we propose a novel strategy for optimizing decentralized energy exchange in digitalized power grids, i.e., the Smart Grid. The strategy considers prosumer’s involvement, energy loss of delivery, network topology, and physical constraints of distribution networks. To evaluate the solution, we build a simulation program and design three meaningful evaluation cases according to different energy flow patterns. The simulation results indicate that, compared to traditional power distribution system, the maximum reduction of energy loss, energy costs, energy provided by the electric utility based using the proposed strategy can reach 51 % , 66 % , 97.5 % , depending on the strategy. Moreover, the proportion of energy self-satisfaction approaches reaches 98 % . Full article
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
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Open AccessArticle Earliest Deadline Control of a Group of Heat Pumps with a Single Energy Source
Energies 2016, 9(7), 552; https://doi.org/10.3390/en9070552
Received: 4 May 2016 / Revised: 18 June 2016 / Accepted: 5 July 2016 / Published: 15 July 2016
Cited by 1 | PDF Full-text (766 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we develop and investigate the optimal control of a group of 104 heat pumps and a central Combined Heat and Power unit (CHP). The heat pumps supply space heating and domestic hot water to households. Each house has a buffer
[...] Read more.
In this paper, we develop and investigate the optimal control of a group of 104 heat pumps and a central Combined Heat and Power unit (CHP). The heat pumps supply space heating and domestic hot water to households. Each house has a buffer for domestic hot water and a floor heating system for space heating. Electricity for the heat pumps is generated by a central CHP unit, which also provides thermal energy to a district heating system. The paper reviews recent smart grid control approaches for central and distributed levels. An online algorithm is described based on the earliest deadline first theory that can be used on the aggregator level to control the CHP and to give signals to the heat pump controllers if they should start or should wait. The central controller requires only a limited amount of privacy-insensitive information from the heat pump controllers about their deadlines, which the heat pump controllers calculate for themselves by model predictions. In this way, a robust heat pump and CHP control is obtained, which is able to minimize energy demand and results in the desired thermal comfort for the households. The simulations demonstrate fast computation times due to minor computational and communication overheads. Full article
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
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Open AccessArticle Harnessing the Flexibility of Thermostatic Loads in Microgrids with Solar Power Generation
Energies 2016, 9(7), 547; https://doi.org/10.3390/en9070547
Received: 21 April 2016 / Revised: 15 June 2016 / Accepted: 7 July 2016 / Published: 15 July 2016
Cited by 4 | PDF Full-text (779 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a demand response (DR) framework that intertwines thermodynamic building models with a genetic algorithm (GA)-based optimization method. The framework optimizes heating/cooling schedules of end-users inside a business park microgrid with local distributed generation from renewable energy sources (DG-RES) based on
[...] Read more.
This paper presents a demand response (DR) framework that intertwines thermodynamic building models with a genetic algorithm (GA)-based optimization method. The framework optimizes heating/cooling schedules of end-users inside a business park microgrid with local distributed generation from renewable energy sources (DG-RES) based on two separate objectives: net load minimization and electricity cost minimization. DG-RES is treated as a curtailable resource in anticipation of future scenarios where the infeed of DG-RES to the regional distribution network could be limited. We test the DR framework with a case study of a refrigerated warehouse and an office building located in a business park with local PV generation. Results show the technical potential of the DR framework in harnessing the flexibility of the thermal masses from end-user sites in order to: (1) reduce the energy exchange at the point of connection; (2) reduce the cost of electricity for the microgrid end-users; and (3) increase the local utilization of DG-RES in cases where DG-RES exports to the grid are restricted. The results of this work can aid end-users and distribution network operators to reduce energy costs and energy consumption. Full article
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
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Open AccessArticle Electricity Self-Sufficient Community Clustering for Energy Resilience
Energies 2016, 9(7), 543; https://doi.org/10.3390/en9070543
Received: 23 April 2016 / Revised: 25 June 2016 / Accepted: 7 July 2016 / Published: 14 July 2016
Cited by 3 | PDF Full-text (5317 KB) | HTML Full-text | XML Full-text
Abstract
Local electricity generation and sharing has been given considerable attention recently for its disaster resilience and other reasons. However, the process of designing local sharing communities (or local grids) is still unclear. Thus, this study empirically compares algorithms for electricity sharing community clustering
[...] Read more.
Local electricity generation and sharing has been given considerable attention recently for its disaster resilience and other reasons. However, the process of designing local sharing communities (or local grids) is still unclear. Thus, this study empirically compares algorithms for electricity sharing community clustering in terms of self-sufficiency, sharing cost, and stability. The comparison is performed for all 12 months of a typical year in Yokohama, Japan. The analysis results indicate that, while each individual algorithm has some advantages, an exhaustive algorithm provides clusters that are highly self-sufficient. The exhaustive algorithm further demonstrates that a clustering result optimized for one month is available across many months without losing self-sufficiency. In fact, the clusters achieve complete self-sufficiency for five months in spring and autumn, when electricity demands are lower. Full article
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
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Open AccessArticle Real Time Information Based Energy Management Using Customer Preferences and Dynamic Pricing in Smart Homes
Energies 2016, 9(7), 542; https://doi.org/10.3390/en9070542
Received: 10 March 2016 / Revised: 26 June 2016 / Accepted: 5 July 2016 / Published: 14 July 2016
Cited by 12 | PDF Full-text (1082 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents real time information based energy management algorithms to reduce electricity cost and peak to average ratio (PAR) while preserving user comfort in a smart home. We categorize household appliances into thermostatically controlled (tc), user aware (ua),
[...] Read more.
This paper presents real time information based energy management algorithms to reduce electricity cost and peak to average ratio (PAR) while preserving user comfort in a smart home. We categorize household appliances into thermostatically controlled (tc), user aware (ua), elastic (el), inelastic (iel) and regular (r) appliances/loads. An optimization problem is formulated to reduce electricity cost by determining the optimal use of household appliances. The operational schedules of these appliances are optimized in response to the electricity price signals and customer preferences to maximize electricity cost saving and user comfort while minimizing curtailed energy. Mathematical optimization models of tc appliances, i.e., air-conditioner and refrigerator, are proposed which are solved by using intelligent programmable communication thermostat ( iPCT). We add extra intelligence to conventional programmable communication thermostat (CPCT) by using genetic algorithm (GA) to control tc appliances under comfort constraints. The optimization models for ua, el, and iel appliances are solved subject to electricity cost minimization and PAR reduction. Considering user comfort, el appliances are considered where users can adjust appliance waiting time to increase or decrease their comfort level. Furthermore, energy demand of r appliances is fulfilled via local supply where the major objective is to reduce the fuel cost of various generators by proper scheduling. Simulation results show that the proposed algorithms efficiently schedule the energy demand of all types of appliances by considering identified constraints (i.e., PAR, variable prices, temperature, capacity limit and waiting time). Full article
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
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Open AccessArticle Optimal Coordinated Management of a Plug-In Electric Vehicle Charging Station under a Flexible Penalty Contract for Voltage Security
Energies 2016, 9(7), 538; https://doi.org/10.3390/en9070538
Received: 30 March 2016 / Revised: 27 June 2016 / Accepted: 8 July 2016 / Published: 13 July 2016
Cited by 5 | PDF Full-text (3320 KB) | HTML Full-text | XML Full-text
Abstract
The increasing penetration of plug-in electric vehicles (PEVs) may cause a low-voltage problem in the distribution network. In particular, the introduction of charging stations where multiple PEVs are simultaneously charged at the same bus can aggravate the low-voltage problem. Unlike a distribution network
[...] Read more.
The increasing penetration of plug-in electric vehicles (PEVs) may cause a low-voltage problem in the distribution network. In particular, the introduction of charging stations where multiple PEVs are simultaneously charged at the same bus can aggravate the low-voltage problem. Unlike a distribution network operator (DNO) who has the overall responsibility for stable and reliable network operation, a charging station operator (CSO) may schedule PEV charging without consideration for the resulting severe voltage drop. Therefore, there is a need for the DNO to impose a coordination measure to induce the CSO to adjust its charging schedule to help mitigate the voltage problem. Although the current time-of-use (TOU) tariff is an indirect coordination measure that can motivate the CSO to shift its charging demand to off-peak time by imposing a high rate at the peak time, it is limited by its rigidity in that the network voltage condition cannot be flexibly reflected in the tariff. Therefore, a flexible penalty contract (FPC) for voltage security to be used as a direct coordination measure is proposed. In addition, the optimal coordinated management is formulated. Using the Pacific Gas and Electric Company (PG&E) 69-bus test distribution network, the effectiveness of the coordination was verified by comparison with the current TOU tariff. Full article
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
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Open AccessArticle Energy Optimization and Management of Demand Response Interactions in a Smart Campus
Energies 2016, 9(6), 398; https://doi.org/10.3390/en9060398
Received: 29 March 2016 / Revised: 25 April 2016 / Accepted: 18 May 2016 / Published: 25 May 2016
Cited by 9 | PDF Full-text (1739 KB) | HTML Full-text | XML Full-text
Abstract
The proposed framework enables innovative power management in smart campuses, integrating local renewable energy sources, battery banks and controllable loads and supporting Demand Response interactions with the electricity grid operators. The paper describes each system component: the Energy Management System responsible for power
[...] Read more.
The proposed framework enables innovative power management in smart campuses, integrating local renewable energy sources, battery banks and controllable loads and supporting Demand Response interactions with the electricity grid operators. The paper describes each system component: the Energy Management System responsible for power usage scheduling, the telecommunication infrastructure in charge of data exchanging and the integrated data repository devoted to information storage. We also discuss the relevant use cases and validate the framework in a few deployed demonstrators. Full article
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
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Open AccessArticle Optimal Bidding Strategy for Renewable Microgrid with Active Network Management
Energies 2016, 9(1), 48; https://doi.org/10.3390/en9010048
Received: 24 November 2015 / Revised: 30 December 2015 / Accepted: 11 January 2016 / Published: 15 January 2016
Cited by 3 | PDF Full-text (2445 KB) | HTML Full-text | XML Full-text
Abstract
Active Network Management (ANM) enables a microgrid to optimally dispatch the active/reactive power of its Renewable Distributed Generation (RDG) and Battery Energy Storage System (BESS) units in real time. Thus, a microgrid with high penetration of RDGs can handle their uncertainties and variabilities
[...] Read more.
Active Network Management (ANM) enables a microgrid to optimally dispatch the active/reactive power of its Renewable Distributed Generation (RDG) and Battery Energy Storage System (BESS) units in real time. Thus, a microgrid with high penetration of RDGs can handle their uncertainties and variabilities to achieve the stable operation using ANM. However, the actual power flow in the line connecting the main grid and microgrid may deviate significantly from the day-ahead bids if the bids are determined without consideration of the real-time adjustment through ANM, which will lead to a substantial imbalance cost. Therefore, this study proposes a formulation for obtaining an optimal bidding which reflects the change of power flow in the connecting line by real-time adjustment using ANM. The proposed formulation maximizes the expected profit of the microgrid considering various network and physical constraints. The effectiveness of the proposed bidding strategy is verified through the simulations with a 33-bus test microgrid. The simulation results show that the proposed bidding strategy improves the expected operating profit by reducing the imbalance cost to a greater degree compared to the basic bidding strategy without consideration of ANM. Full article
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
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Open AccessArticle Cyber Risk Assessment of Transmission Lines in Smart Grids
Energies 2015, 8(12), 13796-13810; https://doi.org/10.3390/en81212393
Received: 22 October 2015 / Revised: 26 November 2015 / Accepted: 27 November 2015 / Published: 4 December 2015
Cited by 5 | PDF Full-text (1081 KB) | HTML Full-text | XML Full-text
Abstract
The increasing use of information technologies in power systems has increased the risk of power systems to cyber-attacks. In this paper, we assess the risk of transmission lines being overloaded due to cyber-based false data injection attacks. The cyber risk assessment is formulated
[...] Read more.
The increasing use of information technologies in power systems has increased the risk of power systems to cyber-attacks. In this paper, we assess the risk of transmission lines being overloaded due to cyber-based false data injection attacks. The cyber risk assessment is formulated as bilevel optimization problems that determine the maximum line flows under false data injection attacks. We propose efficient techniques to reduce the computation complexity of solving the bilevel problems. Specifically, primary and secondary filtering techniques are employed to identify the lines whose flows will never exceed their limits, which can significantly reduce computation burden. A special feasibility cut-based acceleration technique is introduced to further reduce the computation burden. The simulation results on the IEEE 30-bus, IEEE 118-bus, IEEE 300-bus and IEEE 2383-bus systems verify the proposed risk assessment model and the effectiveness of the proposed filtering and acceleration techniques. Full article
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
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Open AccessArticle A Novel, Stable, and Economic Power Sharing Scheme for an Autonomous Microgrid in the Energy Internet
Energies 2015, 8(11), 12741-12764; https://doi.org/10.3390/en81112338
Received: 16 September 2015 / Revised: 2 November 2015 / Accepted: 4 November 2015 / Published: 11 November 2015
Cited by 13 | PDF Full-text (2989 KB) | HTML Full-text | XML Full-text
Abstract
With a higher penetration of distributed generation in the power system, the application of microgrids is expected to increase dramatically in the future. This paper proposes a novel method to design optimal droop coefficients of dispatchable distributed energy resources for a microgrid in
[...] Read more.
With a higher penetration of distributed generation in the power system, the application of microgrids is expected to increase dramatically in the future. This paper proposes a novel method to design optimal droop coefficients of dispatchable distributed energy resources for a microgrid in the Energy Internet considering the volatility of renewable energy generation, such as wind and photovoltaics. The uncertainties of renewable energy generation are modeled by a limited number of scenarios with high probabilities. In order to achieve stable and economical operation of a microgrid that is also suitable for plug-and-play distributed renewable energy and distributed energy storage devices, a multi-objective optimization model of droop coefficients compromising between operational cost and the integral of time-weighted absolute error criterion is developed. The optimization is solved by using a differential evolution algorithm. Case studies demonstrate that the economy and transient behavior of microgrids in the Energy Internet can both be improved significantly using the proposed method. Full article
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
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Open AccessArticle Convergent Double Auction Mechanism for a Prosumers’ Decentralized Smart Grid
Energies 2015, 8(11), 12342-12361; https://doi.org/10.3390/en81112315
Received: 2 August 2015 / Revised: 29 September 2015 / Accepted: 19 October 2015 / Published: 30 October 2015
Cited by 3 | PDF Full-text (977 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we propose a novel automated double auction mechanism called convergent linear function submission-based double-auction (CLFS-DA) for a prosumers’ decentralized smart grid. The target decentralized smart grid is a regional electricity network that consists of many prosumers that have a battery
[...] Read more.
In this paper, we propose a novel automated double auction mechanism called convergent linear function submission-based double-auction (CLFS-DA) for a prosumers’ decentralized smart grid. The target decentralized smart grid is a regional electricity network that consists of many prosumers that have a battery and a renewable energy-based generator, such as photovoltaic cells. In the proposed double-auction mechanism, each intelligent software agent representing each prosumer submits linear demand and supply functions to an automated regional electricity market where they are registered. It is proven that the CLFS-DA mechanism is guaranteed to obtain one of the global optimal price profiles in addition to it achieving an exact balance between demand and supply, even through the learning period. The proof of convergence is provided on the basis of the theory of LFS-DA, which gives a clear bridge between a function submission-based double auction and a dual decomposition (DD)-based real-time pricing procedure. The performance of the proposed mechanism is demonstrated numerically through a simulation experiment. Full article
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
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Open AccessArticle Data-Driven Baseline Estimation of Residential Buildings for Demand Response
Energies 2015, 8(9), 10239-10259; https://doi.org/10.3390/en80910239
Received: 10 July 2015 / Revised: 25 August 2015 / Accepted: 7 September 2015 / Published: 17 September 2015
Cited by 9 | PDF Full-text (1572 KB) | HTML Full-text | XML Full-text
Abstract
The advent of advanced metering infrastructure (AMI) generates a large volume of data related with energy service. This paper exploits data mining approach for customer baseline load (CBL) estimation in demand response (DR) management. CBL plays a significant role in measurement and verification
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The advent of advanced metering infrastructure (AMI) generates a large volume of data related with energy service. This paper exploits data mining approach for customer baseline load (CBL) estimation in demand response (DR) management. CBL plays a significant role in measurement and verification process, which quantifies the amount of demand reduction and authenticates the performance. The proposed data-driven baseline modeling is based on the unsupervised learning technique. Specifically we leverage both the self organizing map (SOM) and K-means clustering for accurate estimation. This two-level approach efficiently reduces the large data set into representative weight vectors in SOM, and then these weight vectors are clustered by K-means clustering to find the load pattern that would be similar to the potential load pattern of the DR event day. To verify the proposed method, we conduct nationwide scale experiments where three major cities’ residential consumption is monitored by smart meters. Our evaluation compares the proposed solution with the various types of day matching techniques, showing that our approach outperforms the existing methods by up to a 68.5% lower error rate. Full article
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
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Open AccessReview Electrical Market Management Considering Power System Constraints in Smart Distribution Grids
Energies 2016, 9(6), 405; https://doi.org/10.3390/en9060405
Received: 30 March 2016 / Revised: 9 May 2016 / Accepted: 9 May 2016 / Published: 25 May 2016
Cited by 5 | PDF Full-text (1444 KB) | HTML Full-text | XML Full-text
Abstract
Rising demand, climate change, growing fuel costs, outdated power system infrastructures, and new power generation technologies have made renewable distribution generators very attractive in recent years. Because of the increasing penetration level of renewable energy sources in addition to the growth of new
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Rising demand, climate change, growing fuel costs, outdated power system infrastructures, and new power generation technologies have made renewable distribution generators very attractive in recent years. Because of the increasing penetration level of renewable energy sources in addition to the growth of new electrical demand sectors, such as electrical vehicles, the power system may face serious problems and challenges in the near future. A revolutionary new power grid system, called smart grid, has been developed as a solution to these problems. The smart grid, equipped with modern communication and computation infrastructures, can coordinate different parts of the power system to enhance energy efficiency, reliability, and quality, while decreasing the energy cost. Since conventional distribution networks lack smart infrastructures, much research has been recently done in the distribution part of the smart grid, called smart distribution grid (SDG). This paper surveys contemporary literature in SDG from the perspective of the electricity market in addition to power system considerations. For this purpose, this paper reviews current demand side management methods, supply side management methods, and electrical vehicle charging and discharging techniques in SDG and also discusses their drawbacks. We also present future research directions to tackle new and existing challenges in the SDG. Full article
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
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