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Real-Time Load Scheduling and Storage Management for Solar Powered Network Connected EVs

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Abstract

In this paper, we investigate a joint real-time load scheduling and energy storage management at a grid-connected solar powered electric vehicle. Without any a-priori knowledge, we consider a finite time approach with arbitrary dynamics of system inputs. Our aim is to minimize an average aggregated system cost through joint optimization of electric vehicle's energy procurement price, load scheduling delays, photovoltaic sufficiency in terms of locally generated renewable energy mix, and battery degradation. Through subsequent modification and reformulation of the joint optimization problem, we utilize the concept of one-slot look-ahead queue stability to solve the problem by employing the Lyapunov optimization technique. We show that the joint optimization problem is separable into sub-problems which are sequentially solved with asymptotic optimality and a bounded performance guarantee. Simulations are carried in different scenarios and under varying weather conditions. Results show that our proposed algorithm can achieve a daily electric vehicle's photovoltaic sufficiency up to 50.50%, a monthly bill reduction up to 72.61%, and a yearly reduced CO $_2$ emission level up to 6.06 kg, while meeting electric vehicle user's energy and delay requirements.

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... In addition, ER-PEM enables cost-effective solutions for smart users considering QoS, energy transactions between ER and utility grid, PV energy, and storage system. However, a limited amount of work has been done in the above context and most of the literature has either focused on communication and control aspects [5], [6], [7], [9], [13] or on energy management aspects of ER [11], [12], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23]. For instance, authorsin [5] and [6], described the role of the ER in EI networks, investigated design challenges in terms of communication typologies, governance models, and security concerns. ...
... Tu et al. [13] proposed a modular-based ER strategy for connecting dc micro grid clusters with ac grids. The other references investigated the operation of ER based on the management aspects [11], [12], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], for example, authors in [11] and [12] evaluated the QoS metric for load allocation problem using PEM system. Li et al. [14] proposed an optimizationbased strategy for the integrated energy system to minimize the cost of the EH using ER applications. ...
... In [15], the authors examined optimization problems for home energy management systems (HEMS) in the context of the EH, while the authors in [16] and [17] formulated an energy management solution for operational costs and CO 2 minimization considering contingency constraints in microgrids. Ahmad and Khan [18] solved the joint optimization problem through Lyapunov optimization considering renewable sources, loads, and energy procurement prices, whereas Carli et al. in [19] proposed scheduling algorithms for solving an online optimization problem in microgrids and daily cost minimization through the DSM was achieved. Demand response (DR) methods were proposed in [20] and [21] to control the peak to average ratio (PAR) and to reduce systems costs, while the authors in [22] and [23] investigated HEMS with DSM to reduce energy costs and peak power consumption, considering user's requirements over a finite time horizon. ...
Article
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This article presents an ER-based PEM strategy for PV integrated smart homes to jointly optimize their load scheduling delays, energy transactions cost, and battery degradation cost. The proposed approach incorporates a MA case, where, the ER acts as a main selecting agent realized by all other system elements. This leads to a combinatorial optimization problem, which can be effectively solved by heuristic optimization methods (HOMs), namely, genetic algorithm (GA), binary particle swarm optimization (BPSO), differential evolution (DE) algorithm, and harmony search algorithm (HSA). Specifically, we investigate the impact of the hyperparameters of the HOMs on the designed ER-based PEM system. Simulations are carried out for multiple smart homes under varying weather conditions to evaluate the effectiveness of HOMs in terms of selected performance metrics. Results show that the ER-based PEM reduces the average aggregated system cost, ensures economic benefits by selling surplus energy, while meeting customers energy packet demand, satisfying their quality-of-service, and operational constraints.
... In remote areas, where power grid extendibility is costly and not possible, the mini grid concept is effective and reliable [5]. Micro grid (MG) is a connected group of dispersed energy generators, renewable sources and manageable loads within a resident and define area operative self-reliantly. ...
... Building has a mix energy system comprise of energy storages, renewable sources, photo voltaic (PV) and different appliances. The authors in [4]- [5] have worked on management of storage and realtime load scheduling for renewable energy (RE) integrated electric vehicle (EV) with aims of energy gaining price minimization from aboard PV, parking lots, public charge stations and home charge stations, delay minimization, load scheduling, PV sufficiency and battery degradation cost reduction and CO2 reduction in air. In [5], authors have proposed a model for management of energy storage and load scheduling. ...
... The authors in [4]- [5] have worked on management of storage and realtime load scheduling for renewable energy (RE) integrated electric vehicle (EV) with aims of energy gaining price minimization from aboard PV, parking lots, public charge stations and home charge stations, delay minimization, load scheduling, PV sufficiency and battery degradation cost reduction and CO2 reduction in air. In [5], authors have proposed a model for management of energy storage and load scheduling. The user comfort is taken into problem aim for external grid connected solar integrated smart home and [6] has worked and simulated roof top isolated PV MG with real time storage and load planning and energy gaining price minimization from peaked generator Fig.1 and Fig.2 is system model of the [5] and [6] research work. ...
... In [17], authors have formulated the problem of energy management by means of convex optimization, a mathematical optimization, taking the economic dispatch into consideration, a real time and reliable power management is adopted in distributed manner. In [18], authors have developed a load scheduling and energy management model for electric vehicle, which connected to external grid and charging stations. In remote areas, where power grid extendibility is costly and not possible, the mini grid concept is effective and reliable [19]. ...
... In remote areas, where power grid extendibility is costly and not possible, the mini grid concept is effective and reliable [19]. The authors in [18]- [20] have worked on management of storage and real-time load scheduling of RE integrated electric vehicle (EV). ...
... EV Onboard PV generated energy is used for driving tasks and remaining is stored in electric vehicle storage system (EVSS). We suppose that the procured amount of energy from the PV system fitted at EV is first used for driving and motor loads [18], while the remaining energy is stored in EVSS. The generated amount of energy is , such that (4.20) ...
Thesis
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Existing power grids (PGs) and in-home energy management controllers do not offer their users the choices to maintain comfort and provide a bearable solution in terms of reduced carbon emission and low cost. Moreover, implementing a real-time price-based demand response program and integrating renewable energy sources (RESs) improve efficiency and ensure the stability of the electric grid. The energy transition is a road map toward the transformation of the existing power grids (PGs) from fossil-based to low carbon, low cost, and sustainable smart grids. With the advent of the smart grid, consumers can schedule their appliances usage pattern to participate in demand-side management (DSM) in response to the utility demand response (DR) program. This way, not only do the users reduce their energy bill and enhance the comfort level, but the utility company can control peak hour’s demand and can reduce carbon emission (CE). In this research work, a novel optimization-based demand-side management (DSM) framework is proposed for a smart grid with integrated renewables. The proposed DSM framework adjusts consumers' energy usage behavior using a real-time price-based demand response program (RTPDRP) to create an operational plan. Consumers implement the generated plan to minimize energy cost, peak load, and carbon emissions while improving user comfort (UC) and avoiding rebound peaks. The simulations are performed using our proposed hybrid genetic particle-driven optimization (HGPDO) algorithm to generate a schedule for residential consumers. The proposed DSM framework based on the HGPDO algorithm is validated with five existing algorithms-based frameworks. Simulation results show that the proposed DSM framework is superior compared to the existing frameworks in terms of minimizing energy cost, mitigating peak loads, reducing carbon emissions, and minimizing user inconvenience. This research work is based on energy usage scheduling and energy management under electric utility and renewable energy sources i.e., solar, thermal, controllable heat and power (CHP), and wind energy (WE) together. Efficient integration of renewable energy sources (RESs) and battery storage systems (BSS) will fill the energy demand at peak price hours. User’s electricity bills will reduce in favor of scheduling their appliances and changing the usage patterns of schedulable electric appliances. Moreover, user comfort (UC) i.e., delay comfort, visual comfort (VC), thermal comfort (TC), and air quality comfort (AQC) is improved. Users will benefit from low-price energies, such as solar, CHP, and wind energy besides the maximum comfort. The shift in demand to low peak hours will reduce the peak to average ratio (PAR) of power and incorporate into the system, lowering outage and failure chances. Integration of renewable energy will cause bill reduction and decrease of carbon emission into the environment. In this context, load scheduling and energy management controller (LSEMC) is proposed which works on heuristic algorithms i.e., genetic algorithm (GA), wind-driven optimization (WDO), bacterial foraging optimization (BFO), binary particle swarm optimization (BPSO), and our designed HGPDO algorithm. The performance of heuristic algorithms and the proposed system is evaluated via MATLAB simulations. Results demonstrate that the efficient integration of BSS and RES decreases the PAR, electricity bill cost, and CO2 for RTPDRP in Case 1, by 52.78%, 58.69%, and 72.40%, in Case 2, by 45.02%, 47.55%, and 92.90%, and in Case 3, by 54.35%, 33.6%, and 91.64%, respectively as compared with unscheduled. The user comfort by our proposed HGPDO algorithm in terms of delay, thermal, air quality, and visual for RTPDRP improves by 35.55%, 16.66%, 91.64%, and 45%, respectively. Moreover, the performance of the heuristic algorithms and the proposed scheme is evaluated via simulations for flat price in Scenario 1 and for day a-head price in Scenario 2. Results demonstrate that the proposed algorithm-based LSEMC reduces the electricity bill, peak to average ratio, and CE in Scenario 1, by 25.7%, 36.39%, and 20.74% and in Scenario 2, by 35.25%, 31.72%, and 36.30%, respectively as compared with without the LSEMC. Moreover, the user comfort in terms of overall delay, thermal, air quality, and visual also improves in Scenario 1, by 26.77%, 13.33%, 3.28%, and 31.66%, and in Scenario 2, by 23.33%, 10%, 3.30%, and 45%, respectively. Keywords: smart grid, load scheduling, energy management, renewable energy, hybrid heuristic algorithms.
... Their goals were to minimise PAR, energy bill costs, and carbon emissions. In [38], authors have proposed an energy management and load scheduling framework for EVs, which were tied to the charging stations and power grid. The micro-grid idea is practical and reliable in remote areas where electricity line extension is expensive and not possible, the authors in [39] suggested a solar-powered and grid-connected smart home model. ...
... Their objectives were to schedule the load, control storage, and achieve maximum indoor user comfort. The authors in [38]- [40] have focused on renewable energy integrated EVs and smart building, storage management, user comfort, and real-time load scheduling. Their major goals were to reduce energy consumption from external power grid, reduce EV charging from the utility grid and charge stations such as parking lots, public and residential charging stations. ...
... The energy generated by EV on-board PV is utilised to operate the vehicle and loads, with the surplus being stored in an electric vehicle battery (EVB). The procured energy from the PV source installed on the EV is expected to be utilized firstly for driving tasks, i.e., lights and motor loads [38], with the residual part of energy being stored in EV battery. Let gt is the quantity of energy harvested by the EV's on-board PV system given below: ...
Article
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The advancement of the smart grids (SGs) is enabling consumers to schedule home appliances to respond to demand response programs (DRs) offered by distribution system operators (DSOs). This way, not only will customers save money on their energy bills and be more comfortable, but the utility company will also be able to regulate peak-hour demand and reduce carbon emissions (CE). Designing an optimization scheme to reduce the electricity bill cost, peak-to-average ratio (PAR), CO2 emission, wait time, and enhance the user comfort in terms of delay, luminance, and thermal comfort is not only the aim of this work but also the need of demand-side management. This research focuses on energy usage, scheduling, and management under the DR program of an electric utility, as well as renewable energy sources integration, i.e., solar energy (SE), thermal, controllable heat and power (CHP), and wind energy (WE). Moreover, the integration of renewable energy sources will reduce electricity bills and also lower the environmental impact of CE. In this context, a smart appliances scheduler and energy management controller (ASEMC) is proposed which is based on heuristic algorithms, i.e., genetic algorithm (GA), wind-driven optimization (WDO), particle swarm optimization (PSO), bacterial foraging optimization (BFO) and our proposed hybrid of GA, PSO, and WDO (HGPDO) algorithm. The performance of the proposed scheme and heuristic algorithms is evaluated via simulations. Results show that in Scenario 1, the proposed algorithm-based ASEMC reduces the electricity bill costs, PAR, and CE by 25.7%, 36.39%, and 20.74%, respectively, while in Scenario 2, the proposed algorithm-based ASEMC reduces the electricity bill costs, PAR, and CE by 35.25%, 31.72%, and 36.30%, respectively. Furthermore, in Scenario 1, user comfort in terms of cumulative delay, indoor air freshness quality, thermal, and visual comfort improves by 26.77%, 3.28%, 13.33%, and 31.66%, whereas in Scenario 2, user comfort improves by 23.33%, 3.30%, 10%, and 45%, respectively.
... Their objectives were to reduce cost, PAR and carbon emission. In [13], authors have developed a load scheduling and energy management model for electric vehicle, which was connected to external grid and charging stations. In remote areas, where power grid extendibility is costly and not feasible, the mini grid concept is effective and reliable [14]. ...
... In remote areas, where power grid extendibility is costly and not feasible, the mini grid concept is effective and reliable [14]. The authors in [13]- [15] have worked on management of storage and real-time load scheduling of renewable energy (RE) integrated electric vehicle (EV). Their main objectives were to minimize energy gaining from external grid and charge stations which is parking station, public and home charge stations. ...
... where S sch m∈M (t) represent the indication of on/off state of M number SSA and S nsch n∈N (t) represent the on/off state of N number NSA, and EP(t) is electricity price in the particular time slot t. The electricity bill L at any time slot t after taking all RES and BSS into consideration is calculated as following (13) where the τ is the duration when the RESs are either not available or sufficient, so the load drains energy from BSS. Our main objective function is given in (14) below. ...
Article
Full-text available
Existing power grids (PGs) and in-home energy management controllers do not offer its users the choice to maintain comfort and provide a bearable solution in terms of low cost and reduced carbon emission. This work is based on energy usage scheduling and management under electric utility and renewable energy sources i.e., solar energy (SE), controllable heat and power (CHP) and wind energy (WE) together. Efficient integration of renewable energy sources (RES) and battery storage system (BSS) have been suggested to solve the energy management problem, reduce the bill cost, peak-to-average ratio (PAR) and carbon emission. User’s electricity bill reduction have been achieved by proposed power usage scheduling method and integrating low cost RESs. PAR minimization have been achieved through shifting the demand in response to real time price from high-peak hours to low-peak hours. In this context, load scheduling and energy storage system management controller (LSEMC) is proposed which is based on heuristic algorithms i.e., genetic algorithm (GA), wind driven optimization (WDO), binary particle swarm optimization (BPSO), bacterial foraging optimization (BFO) and our suggested hybrid of GA, WDO and PSO (HGPDO) algorithm. The performance of the heuristic algorithms and proposed scheme is evaluated numerically. Results demonstrate that our proposed algorithm and the LSEMC reduces the electricity bill, PAR and CO2 in Case 1, by 58.69%, 52.78% and 72.40%, in Case 2, by 47.55%, 45.02% and 92.90% and in Case 3, by 33.6%, 54.35% and 91.64%, respectively as compared with unscheduled. Moreover, the user comfort by our proposed HGPDO algorithm in terms of delay, thermal, air quality and visual improves by 35.55%, 16.66%, 91.64% and 45%, respectively.
... Although DER is widely valued, there are still certain problems remaining in its application. Firstly, features of the DG are small capacity and uneven distribution, energy generation from the DER is intermittent and random making energy less reliable [6]; Secondly, when the power generated by DER is directly connected to the grid, these powers are invisible and uncontrollable. If the amount of grid-connected is too large, it is easy to cause load fluctuations, which will cause the power system to lose safety and reliability [7]; Finally, the supply-demand relationship of the power market becomes a bottleneck hindering further advancement of DER. ...
Article
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Aiming at the data authenticity and storage problems in the current coordinated scheduling of virtual power plants, as well as the opaque information and high transaction costs, a dual blockchains security mechanism is proposed to solve above problems. In the process of security scheduling, a hybrid attribute proxy re-encryption algorithm based on ciphertext strategy is designed. The algorithm is composed of an identity encryption algorithm and an attribute proxy re-encryption algorithm with ciphertext strategy. Combining blockchain, tamper-proof smart metering equipment can effectively solve data authenticity and confidentiality in the information transmission process of distributed energy. In the research process of the trading mechanism, a continuous double auction mechanism based on reputation is proposed. In order to create a favorable trading atmosphere, reputation-based market segmentation mechanisms are integrated, and participants are divided according to reputation value. Depending on the properties of the stored information, they are divided into the private blockchain (with coordination scheduling information) and the consortium blockchain (with transaction information). The system analysis shows the reliability of the dual blockchains architecture. The communication and calculation costs of the proxy re-encryption algorithm verify the practicability of the proposed scheme. The case analysis of the auction mechanism declares that the mechanism can operate effective in the electricity trading market.
... As an important energy storage equipment, Houbbadi et al., in [32], introduced an all-night charging strategy to minimize the battery aging cost and enhance EV's driving distance. Considering the random arrival of EV users' charging load, Ahmad et al., in [33], proposed the dynamic scheduling framework. Vaidya et al., in [34], designed a smart EV charging management system to reduce the charging load and energy congestion when a large amount of EV needed to be charged. ...
Article
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The integration of smart grid and Internet of Things (IoT) has been facilitated with the proliferation of electric vehicles (EVs). However, due to EVs’ random mobility and different interests of energy demand, there exists a significant challenge to optimally schedule energy supply in IoT. In this paper, we propose a secure game theoretic scheme for charging EVs supplied by mobile charging stations (MCSs) in IoT, considering the dynamic renewable energy source. Firstly, the charging system composed of MCSs is developed to implement the charging service. Secondly, when the secure charging scheme of EV users is designed, the utility function of each entity in the charging system is formulated to express the trading relationship between EV users and MCSs. Moreover, with consideration of the competition and cooperation, we propose a Stackelberg game framework with sub-noncooperative optimization. Thirdly, the existence and uniqueness of both Stackelberg equilibrium (SE) and Nash equilibrium (NE) are theoretically analyzed and proved. Through the presented distributed energy scheduling algorithm, we can achieve the optimal solution. Finally, numerical results demonstrate the effectiveness and efficiency of our proposal through comparison with other existing schemes.
... HE importance of energy storage in future grids powered by renewable sources, not only for mitigating their intermittency has been widely studied, e.g. by Azizivahed et al. [1] and Byers et al. [2]. Energy storage is also emerging as a measure to alleviate peak power demand to the grid from fast charging stations for electric vehicles, as proposed by Ahmad et al. [3]. With respect to other technologies, electrochemical storage systems (EESSs) present a number of advantages, in terms of site-tolerance, environmental friendliness, noiselessness. ...
Article
An experimental and numerical time-domain analysis of the early electric response of two kw-class Vanadium Redox Flow Batteries (VRFBs) under different state of charge, electrolyte flow and load is presented. The numerical analysis resorted to an equivalent circuit whose parameters were identified from electrochemical impedance spectroscopy measurements. Two discharge modes were investigated: natural discharge on resistors, to detect the spontaneous battery evolution, and discharge driven by an electronic load, forcing a galvanostatic evolution. Two different timescales were investigated in each discharge modes, namely 20ms and 120s. The millisecond mode revealed stack current and voltage swings in the is very short timescale (7 ms). This behavior suggests an initial turbulent electrochemical phase involving successive activations of vanadium species associated with coordination complexes at the positive electrode. In the latter mode, experimental and numerical results revealed that both stacks reached steady conditions much faster, in few hundreds of milliseconds. These results demonstrate that vanadium redox flow batteries are eligible for fast services in 5060 Hz grids, provided the discharge current is driven in a current-source mode by proper interface power electronics. To the best of our knowledge, this is the first time that the fast time-domain response of large VRFBs is reported.
... First, the distributed generator (DG) that uses the RES for power generation has a small capacity and is limited by external conditions, because of which the electricity generated by it is intermittent and random. This significantly reduces the reliability of the power supply [5]. Moreover, when a large number of invisible and uncontrollable power generated by DER directly flow into the power grid, the overall power supply line is prone to overshooting the power flow, which jeopardizes the safety and reliability of the power system [6]. ...
Article
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A growing number of prosumers have entered the local power market in response to an increase in the number of residential users who can afford to install distributed energy resources. The traditional microgrid trading platform has many problems, such as low transaction efficiency, the high cost of market maintenance, opaque transactions, and the difficulty of ensuring user privacy, which are not conducive to encouraging users to participate in local electricity trading. A blockchain-based mechanism of microgrid transactions can solve these problems, but the common single-blockchain framework cannot manage user identity. This study thus proposes a mechanism for secure microgrid transactions based on the hybrid blockchain. A hybrid framework consisting of private blockchain and consortium blockchain is first proposed to complete market transactions. The private blockchain stores the identifying information of users and a review of their transactions, while the consortium blockchain is responsible for storing transaction information. The block digest of the private blockchain is stored in the consortium blockchain to prevent information on the private blockchain from being tampered with by the central node. A reputation evaluation algorithm based on user behavior is then developed to evaluate user reputation, which affects the results of the access audit on the private blockchain. The higher a user’s reputation score is, the more benefits he/she can obtain in the transaction process. Finally, an identity-based proxy signcryption algorithm is proposed to help the intelligent management device with limited computing power obtain signcryption information in the transaction process to protect the transaction information. A system analysis showed that the secure transaction mechanism of the microgrid based on the hybrid blockchain boasts many security features, such as privacy, transparency, and imtamperability. The proposed reputation evaluation algorithm can objectively reflect all users’ behaviors through their reputation scores, and the identity-based proxy signcryption algorithm is practical. 1. Introduction The Energy Internet (EI), a distributed sharing network that combines the Internet and distributed energy resources (DER), can connect many kinds of distributed energy nodes to achieve the two-way flow of energy. Energy is used to provide light, heat, power, and other necessities to human beings. With continual scientific and technological progress, a variety of devices are now available to easily convert electric energy into various kinds of energy needed for human production and living. Therefore, the two-way flow of electric energy will form the core of future research on EI. Currently used forms of primary energy include fossil energy, light energy, wind energy, and water energy [1], whereas electric energy needs to be obtained through conversion from primary energy. The traditional method of conversion is thermal power generation, that is, generating electricity through the combustion of fossil fuels. However, this method is inefficient and causes serious environmental pollution. In 2010, carbon dioxide emissions from energy production, such as the production of electricity, accounted for 76% of global emissions [2]. Considering the importance of environmental protection, research on new methods of conversion has gained momentum. Renewable energy sources (RES) such as light, wind, and water are widely used in the world through primary energy conversion devices. By the end of 2018, the installed capacity of hydropower in China was 352 GW, that of wind power was 184 GW, and that of solar power was 174 GW [3]. In addition, as the number of residential and industrial users who can afford DER deployment, in the form of solar photovoltaic panels, biomass generators, microwind turbines, and diesel engines, grows each year, a growing number of DER are being deployed at the industrial and residential scales [4]. Although DER has the characteristics of low loss, little pollution, and good system economy, it still has problems that need to be solved. First, the distributed generator (DG) that uses the RES for power generation has a small capacity and is limited by external conditions, because of which the electricity generated by it is intermittent and random. This significantly reduces the reliability of the power supply [5]. Moreover, when a large number of invisible and uncontrollable power generated by DER directly flow into the power grid, the overall power supply line is prone to overshooting the power flow, which jeopardizes the safety and reliability of the power system [6]. Finally, the relationship between supply and demand in the power market is a major obstacle to the development of the DER, and consumers’ acceptance of DER power generation needs to be considered. To solve the above problems of DER, two technologies have been proposed: the virtual power plant (VPP) and the microgrid (MG) [7]. The VPP leverages advanced coordinated control technologies, smart metering technologies, and information and communication technologies to interact with participants in EI, thus making full use of the large-scale and multiregional DER. Due to the limitation of the available power transmission technology, long-distance power transmission causes partial power loss. For industrial and residential users who have DG installed, close-range MG technology is a better choice. MG focuses on regional balance of distributed load and power supply to achieve energy autonomy. VPP focuses on realizing the maximum benefit of the main body and has the derivative function of participating in the power market and auxiliary service market [8]. Liu et al. [9] have provided a distributed robust energy management scheme for a system composed of multiple MGs. Uncertain factors in the operation of the MG have been dealt with by tunable robust optimization technology to optimize the total operating cost of the MG, and studies have verified the effectiveness of the method in a four-MG system. Zhang et al. [10] proposed a networked physical–social system for DER management in the MG that has the capability of parallel learning and can promote the emergence of high-quality DER optimization strategies through human–computer interactive learning. A case study was used to show that this technique can yield a DER optimization strategy more quickly than other heuristic algorithms. Ranjbar et al. [11] proposed an MG protection method in which the short-time Fourier transform (STFT) is used to pretreat the voltage waveform within a period, and the features of disturbance are extracted accordingly. These features are fed to a decision tree algorithm to identify fault events in the MG. The results of simulations showed that depending on the type of event, only two or six features were needed to detect any fault. The above literature has mainly focused on solving the technical and economic problems of the MG, but it needs to be further developed to solve issues with its management. The prevalent mode of energy operation mostly uses centralized third-party management organization to manage transactions. This mode of management has the following problems: First, with an increase in the number of DER transactions within its jurisdiction, the operating cost of the trading center increases, transaction efficiency is significantly reduced, and it is difficult to ensure the effective operation of the microgrid in real time. Second, in the energy trading process, the trading center and the trading side cannot achieve complete trust, which imposes a significant annual cost on the trading centers to maintain trust. Moreover, there is no open and transparent trading and information platform in the MG, because of which the security and effectiveness of the transaction cannot be guaranteed, and its cost is high. Finally, the centralized trading center is prone to a single point of failure; that is, the trading center causes the entire system to collapse once it is attacked, and the disclosure or tempering of trading information damages the property and violates the privacy of both parties to the transaction. Since 2016, Bitcoin, a decentralized digital currency, has gained considerable attention from the financial community due to an increase in its economic value. Academics have found that in addition to the economic value of Bitcoin itself, its core supporting technology, namely, the blockchain, has significant research value. The blockchain has the characteristics of decentralization, trustlessness, openness, and transparency. With progress in research, the scope of applications of the blockchain is no longer limited to the financial field. Adding blockchain technology to the transaction process of the MG may provide a new solution to the abovementioned management problems. Research on combining the microgrid energy market with blockchain technology is still in its preliminary stage. To prove the feasibility of this combination, many scholars have carried out a series of studies, and the results show that the blockchain has the ability to support energy transactions within a certain range [12–14]. Based on this assessment of theoretical feasibility, a growing number of papers have been published in the area. Di Silvestre et al. [15] discussed the loss in the distribution of energy transactions of blockchains when applied to the MG and proposed two indicators of loss distribution to solve this problem. The feasibility of these indicators was verified in two operating scenarios of a medium-voltage microgrid. Di Silvestre et al. [16] considered the provision of voltage regulation technology based on the blockchain for the MG, mainly by solving for reactive power optimization power flow and reactive power compensation. The former was intended to ensure optimal economic planning in reactive power production and the latter to evaluate the contribution of voltage regulation. Hassan et al. [17] proposed an energy transaction auction mechanism called differential privacy auction to provide moderately costly but secure and private energy auctions for the MG based on consortium blockchain. Experimental comparisons showed that this mechanism was superior to the VCG mechanism. van Leeuwen et al. [18] designed an integrated energy management platform based on the blockchain that is composed of three parts: a physical layer, economic layer, and information layer. It can facilitate the trade of energy in the microgrid community through a bilateral transaction mechanism and optimize energy flow by solving optimal power flow problems. Meeuw et al. [19] studied the impact of limitations of hardware and the communication infrastructure of applications on the blockchain system. Based on the conditions of the Swiss blockchain-based Walenstadt microgrid, the researchers artificially adjusted the bandwidth between nodes to simulate the bandwidth of the communication infrastructure. They found that a communication network with a bandwidth of less than 1000 kbit/s leads to insufficient system throughput. To solve the problems of default risk and demand uncertainty in designing a renewable energy microgrid based on the blockchain, a method based on robust two-type fuzzy programming was proposed by Tsao et al. [20], and its effectiveness was proved by a case study. The above literature has examined the blockchain-based microgrid system from different technical aspects, but a safe method to protect energy transactions in the MG remains elusive. In this paper, a secure microgrid transaction mechanism based on the blockchain is proposed. The main contributions are as follows: (1)Blockchain-based microgrid trading platforms can solve the problems of trust and transparency in microgrid energy trading, but most schemes proposed in the literature are based on a single blockchain. In application, a single blockchain struggles to provide effective user identity management, and this makes it easier for malicious actors to infiltrate the system. This paper proposes a microgrid energy transaction framework based on the hybrid blockchain containing a trading consortium blockchain and private blockchains for identity management, where is the number of microgrids in the network. Only users verified by the private blockchain can conduct transactions on the consortium blockchain(2)To ensure good market trading behavior, a reputation evaluation algorithm based on user behavior is proposed. Because there are two kinds of identities, buyer and seller, in energy trading, this algorithm contains separate algorithms to assess buyer and seller behaviors. Whether a user can be authenticated by the private blockchain depends on their own reputation: when the reputation has a score of zero, the user cannot use the energy transaction function. In addition, the energy in the consortium blockchain is mainly auctioned by using the continuous double auction algorithm based on reputation. The higher the reputation score of a user is, the more benefit from the transaction they can draw(3)When users participate in energy transactions, they need to communicate with the microgrid continuously. To ensure the security of the information shared during transaction-related communication, an identity-based proxy signcryption algorithm is proposed that is suitable for users with smart home manager. Proxy signcryption allows the smart home manager with a limited amount of computing power and storage to delegate its data processing rights to the powerful energy manager to participate in energy trading. The identity-based proxy signcryption algorithm solves the defect whereby the typical proxy signcryption algorithm needs to store a large number of certificates The remainder of this paper is arranged as follows: Section 2 introduces some preliminary information, and the system as a whole and its detailed framework are introduced in Section 3. In Section 4, we describe the steps of implementation of the proposed scheme, such as details of the buyer and seller reputation evaluation algorithms, the identity-based proxy signcryption process, and the process of generation of new blocks. Section 5 is devoted to a performance analysis and evaluation of the proposed scheme, and we summarize our findings in Section 6. 2. Preliminaries In this section, we review some preliminary knowledge, such as the structure of the microgrid, the nature of the bilinear pairing involved, and the principle of proxy signcrytion. 2.1. Microgrid The earliest concept of the microgrid was proposed by the United States Consortium for Electric Reliability Technology Solutions (CERTS) [21] and remains the most authoritative one. The CERTS microgrid assumes that the set of loads and DER operate as a single system. A critical feature is that it can autonomously exist in the distribution system as a self-controlling entity. In other words, it is impossible to distinguish the MG from legitimate customer sites in the grid. The initial work by the CERTS was based on small-scale micropower sources with a capacity lower than 500 kW, and the basic structure of the MG developed by the CERTS is shown in Figure 1.
... This also works in the presence of PV. EES, HEV [15]. Different scheduling techniques for energy management of residential areas are discussed in [16]. ...
... The EMC output is the optimal power usage schedule of residential building smart appliances. Besides, smart appliances, plugin hybrid electric vehicles (PHEVs), renewable energy sources (RESs), and energy storage systems may penetrate to residential buildings in order to improve sustainability [7,8]. Thus, in-home PHEVs and energy storage systems facilitate consumers to store energy from RESs during daytime and discharge during nighttime to return many benefits from the investment. ...
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This text presents a modern theory of analysis, control, and optimization for dynamic networks. Mathematical techniques of Lyapunov drift and Lyapunov optimization are developed and shown to enable constrained optimization of time averages in general stochastic systems. The focus is on communication and queueing systems, including wireless networks with time-varying channels, mobility, and randomly arriving traffic. A simple drift-plus-penalty framework is used to optimize time averages such as throughput, throughput-utility, power, and distortion. Explicit performance-delay tradeoffs are provided to illustrate the cost of approaching optimality. This theory is also applicable to problems in operations research and economics, where energy-efficient and profit-maximizing decisions must be made without knowing the future. Topics in the text include the following: • Queue stability theory • Backpressure, max-weight, and virtual queue methods • Primal-dual methods for non-convex stochastic utility maximization • Universal scheduling theory for arbitrary sample paths • Approximate and randomized scheduling theory • Optimization of renewal systems and Markov decision systems Detailed examples and numerous problem set questions are provided to reinforce the main concepts.
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Vehicle-to-grid (V2G), the provision of energy and ancillary services from an electric vehicle (EV) to the grid, has the potential to offer financial benefits to EV owners and system benefits to utilities. In this work a V2G algorithm is developed to optimize energy and ancillary services scheduling. The ancillary services considered are load regulation and spinning reserves. The algorithm is developed to be used by an aggregator, which may be a utility or a third party. This algorithm maximizes profits to the aggregator while providing additional system flexibility and peak load shaving to the utility and low costs of EV charging to the customer. The formulation also takes into account unplanned EV departures during the contract periods and compensates accordingly. Simulations using a hypothetical group of 10 000 commuter EVs in the ERCOT system using different battery replacement costs demonstrate these significant benefits.
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We extend stochastic network optimization theory to treat networks with arbitrary sample paths for arrivals, channels, and mobility. The network can experience unexpected link or node failures, traffic bursts, and topology changes, and there are no probabilistic assumptions describing these time varying events. Performance of our scheduling algorithm is compared against an ideal T-slot lookahead policy that can make optimal decisions based on knowledge up to T-slots into the future. We develop a simple non-anticipating algorithm that provides network throughput-utility that is arbitrarily close to (or better than) that of the T-slot lookahead policy, with a tradeoff in the worst case queue backlog kept at any queue. The same policy offers even stronger performance, closely matching that of an ideal infinite lookahead policy, when ergodic assumptions are imposed. Our analysis uses a sample path version of Lyapunov drift and provides a methodology for optimizing time averages in general time-varying optimization problems. Comment: 22 pages
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Hybrid electric vehicles have proved to be the most practical solution in reaching very high fuel economy as well as very low emissions. However, there is no standard solution for the optimal size or ratio of the internal combustion engine and the electric system. The optimum choice includes complex tradeoffs between the heat engine and electric propulsion system on one hand and cost, fuel economy, and performance on the other. Each component, as well as the overall system, have to be optimized to give optimal performance and durability at a low price. In this paper, we look at the effects of hybridization on fuel economy and dynamic performances of vehicles. Different hybridization levels from mild to full hybrid electric traction systems are examined. We also present the optimum level of hybridization for typical passenger cars. This study shows that low hybridization levels provide an acceptable fuel economy benefit at a low price, while the optimal level of hybridization ranges between 0.3 and 0.5, depending on the total vehicle power.
  • A Jain
A. Jain et al., "Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture", arXiv preprint arXiv:1601.00740, 2016.