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For microgrids (MGs) with electric vehicle prosumers, effective time-of-use based energy trading is important for multi-vehicles-to-MG system. In this paper, a Stochastic Stackelberg game (SSG) model is proposed. The model is based on the Stackelberg game, where the sellers act as leader and the buyers are considered as follower. First, according to the distributed generation's (DG) output and load distribution, MGs are classified as sellers and buyers, whose strategies set are established separately. Then the utility models of sellers and buyers are established, which include a stochastic variable model for electric vehicles (EVs). Moreover, with the proof of equilibrium and uniqueness of the Stackelberg equilibrium, sellers are obligated to coordinate the sharing of energy with maximization of the profit, while the buyers are autonomous to maximize their utilities with demands response to availabilities. Finally, the game equilibrium is solved to deal with the uncertainty of EV's energy and plugging-in time. By using the collected data from realistic EVs, the effectiveness of the model is verified in terms of seller profit, the utilities of buyers, and the net energy usage in MG. The results of the static pricing model and SSG model were compared to demonstrate the effectiveness of the SSG model.

This paper investigates the issues of the distributed energy management problem (EMP) of Microgrids in the presence of communication delays. To address this issue, a consensus-based distributed algorithm is proposed to integrate the economic dispatch and demand response, which can optimally assign the energy among generation units and load units with the objective of maximizing the total social welfare of the power system. Different from existing energy management algorithms, a nonuniform time-varying delays model is considered and embedded into the design of our algorithm, such that each unit can achieve collaborative optimization without the requirement of fixed delays information, which has both theoretical merits and practical engineering value for the efficient and stable operation of Microgrids. Moreover, it is proved that the proposed algorithm can converge to the optimal solution under some sufficient conditions. Finally, the correctness and effectiveness of the proposed method are validated by several simulation results.

This paper presents a novel distributed secondary control method for both voltage and frequency regulation in islanded microgrids. Firstly, the large-signal dynamic model of inverter-interfaced distributed generation (DG) is formulated in the form of a multi-input multi-output nonlinear system, which can be converted to a partly linear one using input–output feedback linearization. Then, the linear-distributed model predictive controller is designated in each DG to realize the secondary voltage control by incorporating the forecasted behaviors of the local and neighboring DG units. Through the receding optimization index of every update process, the implementation of optimal control action accelerates the convergence rate for voltage magnitudes to the reference value. Following, after transforming the nonlinear DG dynamics into a first-order linear system, a distributed proportional integral algorithm is introduced in the frequency restoration while maintaining the accurate active power sharing. Our approach utilizes the distributed architecture, which indicates superior reliability and flexibility compared to the centralized manner; moreover, it can accommodate diverse uncertainties in communication links, model parameters, and time delays. Simulation results are provided to verify the effectiveness of the proposed control methodology.

Today, the stability of the electric power grid is maintained through real time balancing of generation and demand. Grid scale energy storage systems are increasingly being deployed to provide grid operators the flexibility needed to maintain this balance. Energy storage also imparts resiliency and robustness to the grid infrastructure. Over the last few years, there has been a significant increase in the deployment of large scale energy storage systems. This growth has been driven by improvements in the cost and performance of energy storage technologies and the need to accommodate distributed generation, as well as incentives and government mandates. Energy management systems (EMSs) and optimization methods are required to effectively and safely utilize energy storage as a flexible grid asset that can provide multiple grid services. The EMS needs to be able to accommodate a variety of use cases and regulatory environments. In this paper, we provide a brief history of grid-scale energy storage, an overview of EMS architectures, and a summary of the leading applications for storage. These serve as a foundation for a discussion of EMS optimization methods and design.

Distribution systems are inevitably vulnerable to natural disasters, which causes multiple damages within or outside the system. The disruption of supply demands microgrid formation, where the objective is to maximize the restoration of critical loads. The complete restoration requires repair of damaged regions which brings the need for an efficient crew selection, repair time estimation, and crew dispatch. Ranking of load points and location are important considerations for strategic placement of distributed generation units. Prioritization of damaged regions is required for efficient crew dispatch. These ranking depends on multiple criteria based on subjective opinions of the experts. However, the existing methods fail to take account of the same. This paper presents fuzzy approaches which fill the gap present in the existing methods by explaining the theoretical solutions to ranking of load points, locations, crew selection, estimation of repair times, and prioritization of the damaged regions.

This paper presents a novel distributed voltage control strategy to maintain the voltage of active distribution networks containing multiple microgrids. Local voltage regulation characteristics, such as power reserve, adjustment cost, and regulating speed, are identified first. According to a neighbors' voltage regulation characteristics, the microgrid coordinates neighborhood resources and asks them for coordination as soon as voltage problems occur. Microgrids schedule local resources based on a cooperative distributed model predictive control algorithm scheme with neighbor-to-neighbor communication. Tests are performed in an active distribution network with three microgrids. The numerical studies have shown that the proposed strategy mitigates the voltage fluctuation rapidly and efficiently with minimum operating cost.

Traditionally, the energy supply in off-grid communities has relied on diesel generating units based microgrids (MGs) but global environmental concerns and advancements in renewable energy technology are pushing for the transformation of these systems into renewable-based MGs. This transformation is a more complex hybrid system, with dispatchable and non-dispatchable resources, and requires novel planning and design tools to ensure the security of supply in the future. This paper presents a methodology that jointly optimizes the capacities and locations of dispatchable and non-dispatchable distributed generating units and battery energy storage system employed in a stand-alone MG serving conventional residential and electric vehicle charging load. The complex dual optimization problem is solved innovatively in two sequential steps. In the first step, the capacities of solar photovoltaic panels, wind turbines, battery energy storage system, and diesel generator are determined without actually considering the system design itself. While in the second step, a novel and simple methodology is developed which determines the sub-optimal capacities of distributed generation units along with their actual locations in the distribution system. To determine the optimal locations of distributed generation units, we defined a novel factor, losses-voltage-factor (LVF), which ensures reduced losses and better voltage quality. The proposed methodology gives a robust design that not only results in reduced losses and better voltage quality but also have higher reliability. The proposed technique is very practical, which can easily be applied for the planning and design of the practical active distribution networks.

Educational buildings consume about 2% of the total energy in a country, which leads to energy cost concerns for building operators. To reduce the building energy cost, an effective way is to intelligently schedule heating, ventilation, and air conditioning (HVAC) systems, which account for above 40% of the total energy consumption in educational buildings. In this paper, we investigate an energy optimization problem for HVAC systems in university campus buildings. To be specific, we first formulate a total cost optimization problem that minimizes the sum of energy cost related to HVAC systems and thermal discomfort cost associated with occupants considering zone occupancy pattern and thermal preference of occupants in each zone. Due to the existence of uncertain parameters as well as spatially and temporally coupled constraints, it is very challenging to solve the formulated problem. To this end, we propose a distributed model predictive control algorithm based on the alternating direction method of multipliers, which has high scalability with an increasing number of zones and can protect user privacy. Extensive simulations based on the real-world traces show that the proposed distributed algorithm could effectively reduce the total cost and offer a flexible tradeoff between energy cost and thermal discomfort.

Modern electric power distribution systems are data rich and include growing numbers of distributed energy resources and distribution automation. To take advantage of distribution automation and manage growing penetrations of distributed energy resources, distribution utilities need applications for planning and operations that use all available data and may incorporate distributed approaches to operate and control. The industry would benefit from distribution management applications based on a common platform that makes systems of each type interchangeable. This paper describes an approach to enabling cost-effective development and deployment of advanced applications for distribution system planning and operations based on development of an open-source, standards-based platform for application development called GridAPPS-D, which leverages data abstractions for application development based on standards, such as the distribution system common information model.

Microgrid is envisioned to be an effective framework to integrate distributed generations, energy storage systems, and various loads. As an important form of distributed generation, renewable generation may change rapidly and frequently, which poses great challenges on the management and control of microgrids. Energy storage systems are often utilized to address the intermittency of renewable generations. This paper proposes an optimal control-based energy management of multiple energy storage system to dynamically minimize the adjustment cost while keeping track of the energy storage system state of charge and maintaining the supply-demand balance in a microgrid. The proposed algorithm has taken the influences of time-of-use price, time-varying distributed generations and loads, transmission loss, energy storage system charging/discharging, and storage efficiency into account. The optimality of the proposed algorithm is guaranteed through rigorous analysis. Simulations on a 5-bus microgrid under various conditions demonstrate the effectiveness of the proposed algorithm.

Energy efficiency in data center networks (DCNs) is critical to the operations of modern large-scale data centers. One effective way is to make the size of DCNs elastic along with flow demands by centralized routing and scheduling, i.e., turning off idle network components to reduce the power consumption. As such, software-defined networking (SDN) is widely used for achieving such elasticity conveniently. Meanwhile, the scale and structure of modern DCNs get much larger and more complex. Central control and global computing become impractical due to the heavy time and space complexity. Therefore, distributed power control is necessary for large-scale SDN-DCNs, and yet there are few research achievements in this area. In this paper, we present an extensible energy-efficient mechanism, which: 1) leverages distributed flow routing for both intraand inter-domain elephant flows and 2) extendedly considers distributed energy efficiency for control plane. A local power-saving function is operated within each domain of control plane, and a distributed energy-efficient routing algorithm is computed to optimize the effectiveness for the inter-domain flows. The simulation results demonstrate that this distributed mechanism applies to large-scale DCNs and achieves an effective power saving. Published version

The integration of distributed generation into distribution systems renders the reliable protection of these systems quite challenging. A solution being considered concerns the application of distance protection to distribution systems, due to its advantages compared to overcurrent protection. However, as distance relays (DRs) are principally designed for transmission systems, some of the factors affecting their operation in such applications have to be extensively studied, focusing on the particularities of these factors in distribution system applications. This paper examines the effect of intermediate infeed and fault resistance on the operation of DRs protecting radial distribution feeders. The investigation of these influencing factors is performed for phase and ground faults through theoretical analysis and simulations in a test distribution feeder, taking into consideration the concurrent effect of line load and zero-sequence compensation during single-line-ground faults. Moreover, the combined effect of intermediate infeed and fault resistance on distance protection is analyzed, and the resulting problems concerning proper relay operation are described. Potential solutions to these problems are proposed.

The need for distributed energy trading is increasing owing to the deployment of distributed renewable generators, and a new class of users, prosumers, based on them. This paper presents two pair-matching strategies for the distributed prosumer energy trading market. We first propose an energy trading rule guaranteeing a minimum trading quantity. The rule manages distributed market risk from unpredictability in the demand and supply of participants. A pair matching problem to maximize social welfare is formulated. However, it requires high computational complexity, as well as a central controller. Therefore, we propose two matching strategies considering the properties of the trading rule and the statistical characteristics of participants by modifying the problem. The proposed strategies can be applied in a distributed manner with a simple mechanism. Numerical results using the ideal and real data sets show that the proposed strategies have near optimal performance, with less than a 2% optimal gap. In addition, we discuss how distributed prosumer energy trading offers a benefit and discuss the dominant parameter for achieving that benefit.

Recently, the academic and industrial literature has arrived at a consensus in which the electric grid evolves to a more intelligent, responsive, dynamic, flexible, and adaptive system. This evolution is caused by several drivers including: decarbonization, electrified transportation, deregulation, growing electricity demand, and active consumer participation. Many of these changes will occur at the periphery of the grid, in the radial distribution system, and its potentially billions of demand-side resources. Such spatially distributed energy resources naturally require equally distributed control and electricity market design approaches to enable an increasingly active “smart grid.”In that regard, this paper serves to highlight lessons recently learned from the literature and point to seven open long-term challenges facing the future design of electricity markets. They are: 1) simultaneously manage the technical and economic performance of the electricity grid; 2) span multiple operations time scales; 3) enable active demand side resources; 4) activate the power grid periphery; 5) promote synergies with interdependent infrastructures; 6) respect organizational jurisdictions; and 7) promote resilient self-healing operation. For each challenge, some recent contributions are highlighted and promising directions for future work are identified.

This paper deals with the use of distributed energy storage systems in microgrids, and proposes a planning method which accounts for the uncertainties of load and distributed generation. Objectives of the planning method are the reduction of the energy costs, while providing the supply of ancillary services as a technical support to the network. The energy costs are evaluated considering a hourly varying pricing scheme and optimizing the storage systems charging/discharging stages. The technical support is devoted to the restraint of bus voltage amplitudes, and of network components' currents/powers within admissible ranges. The input data uncertainties are managed through three decision theory criteria (i.e., the minimization of expected costs; an approach based on the weighted regret felt by the design engineer; and a stability area criterion), which allow considering the multiple design alternatives and futures (i.e., possible values of uncertain input data) in an accurate and feasible way. The design alternatives refer to the size and location of the distributed storage systems, while each future is associated with a different level of load demand and power production of distributed generation over the whole planning period. The results of numerical applications are reported and discussed with reference to a Cigré test network.

The intermittency of distributed generation and the fluctuation of load will derive uncertainties to the decentralized droop-controlled islanded microgrids (IMGs) operation. In order to describe and analyse the probabilistic characteristics of the operating state of an IMG, this paper proposes an analytical method based on cumulants to solve the probabilistic load flow (PLF) for decentralized droop-controlled IMGs, considering the correlation of input variables. Nataf transform is used to deal with the correlation, and the PLF is solved using the cumulant method and the Gram-Charlier series expansion. On this basis, a multi-objective coordinated planning model of active-reactive power resources is presented, considering the annual comprehensive cost and operating risk simultaneously. The Pareto optimal solution set is found using non-dominated sorting genetic algorithm-II to provide a set of alternative planning schemes. The proposed PLF based on cumulant method is compared with Monte Carlo simulation to verify its accuracy and efficiency. Besides, the simulation results also demonstrate that the proposed coordinated planning model of active-reactive power resources can coordinate the security and economy of the IMG operation.

This paper proposes a novel multi-agent bargaining learning (MABL) for the distributed energy hub economic dispatch (EHED) of multiple energy carrier systems (MECS). Distributed EHED is developed by extending the conventional economic dispatch (ED) into MECS in a distributed manner, in which each energy hub is regarded as a learning agent for self-scheduling. The classical Q-learning with associative memory is employed for knowledge learning of each agent, while the non-uniform mutation operator is adopted for handling the continuous control variables. To maximize the total payoff of all the energy hubs, the bargaining game is presented for achieving an effective coordination between the buyer agents and a seller agent, where the slack energy hub is designed as the seller agent and the others are the buyer agents. MABL has been thoroughly evaluated for the distributed EHED on a high-complex 39-hub MECS with 29 energy hub structures and 76 energy production units. Case studies verify the superior performance of MABL for the distributed EHED compared with six centralized heuristic optimization algorithms.

Environmental concerns and depletion of traditional energy lead to the booming development of renewable distributed generation (RDG) in the past decade. However, due to intermittent nature of renewable energy sources, to what extent RDG could provide capacity to power systems becomes a critical issue to the utility company when implementing long-term system strategic (generation expansion) planning. On the other hand, in a smart-grid frame, the popularization of different varieties of demand-side resources enables the system to operate at more flexible modes ever before. The potential variability in load demand not only introduces additional dynamics and uncertainties to the system, but could also affect the reliability benefits of RDG. Therefore, in practice, to effectively estimate the reliability value of RDG in power systems, the potential interaction between generation- and demand-side must be properly captured. In this paper, a study to assess the capacity credit (CC) of RDG in a context of distributed generation system is performed with consideration of the impacts of demand response (DR). A compound reliability model for DR is presented, which considers the uncertainties involved in both instant response and follow-on load recovery processes. On this basis, an assessment framework for the CC of RDG based on sequential Monte Carlo simulation is developed by which the inter-temporal characteristics of DR resources can be fully captured. The numerical study is implemented based on the IEEE-38 bus test case. The calculation results demonstrate that the CC of RDG would depend on a variety of factors, including penetration level, responsiveness of load demand and the correlations between RDG and DR availability. Also, it is shown that accounting for the underlying effect of DR is of absolute importance, otherwise the CC of RDG might be estimated erroneously in real practices.

Featured by distributed energy storing and time division multiplexing transmission, dc packetized-power distribution has great potential in compensating inelastic load fluctuations at the demand side. In this paper, we investigate an ac distribution grid integrated with local area packetized-power networks (LAPPNs). LAPPNs, as the dc sector, enable a store-then-consume mechanism, whereby the sectoral loads characterized as elastic ones, can be manipulated to reduce the distribution level load fluctuation. A multi-mode energy distribution is proposed for the dc sector, including an advanced energy distribution as the ‘storing’ phase utilizing conventionally non-peak hours, and an open energy distribution as the ‘consumption’ phase to further compensate the ac sector load fluctuation on real-time basis. We develop a cross-time hierarchical energy distribution scheme to coordinate energy distribution of dc sector and ac sector and exploit the elasticity of dc sector loads on both day-ahead basis and real-time basis, jointly resulting in a significant mitigation of distribution load fluctuation. Power distribution optimizations are separately formulated and solved for different procedures of the scheme. Simulations have been conducted to demonstrate the effectiveness of the scheme in reducing distribution level load fluctuation and to reveal facts on how much dc elastic loads is needed for fluctuation reduction in a scenario in North America.

The inclusion of distributed renewable energy sources, new goals of efficiency and reliability, and the technological advancement of the power grid has led to a significant increase in the complexity of distribution systems. Although multiple devices can be controlled to achieve these objectives during smart grid operation, many proposed algorithms are based on ideal scenarios where the complex distribution system is assumed to be fully observable or measurable. As this condition obstructs many advanced applications, it is necessary to implement novel alternatives that provide support and validation in the field. In this paper, we show that embedded simulation is capable of providing accurate results of the system real-time condition. The designed platform exploits the technological development of mobile devices, a specific purpose solver, and concurrent processing to embed the power systems simulation into small, flexible, and affordable devices. Simulation results demonstrate the accuracy and timeliness of the proposed real-time simulation based on the IEEE 37 bus test feeder emulation and other large-scale scenarios such as the IEEE 8500 node test feeder. We anticipate that this simulation approach will be useful for applications covering advanced protection relaying, volt–var control, topological reconfiguration, distributed generation management, storage control, and cyber security assessment among others.

The high penetration level of distributed generation (DG) units may lead to various problems and operational limit violations in electric power distribution systems if it exceeds a particular limit known as the system’s hosting capacity (HC). In this paper, the problem of selecting the optimal conductor for a real radial distribution system in Egypt is investigated using a recent meta-heuristic algorithm, known as salp swarm optimization. First, a constrained optimization problem is introduced to minimize the combined annual cost of energy losses and the investment cost of the conductors while complying with the system voltage limits and conductor thermal capacities. The results obtained show the effectiveness of the algorithm in satisfying the objective function and constraints. However, the optimization results also show that a reduction in the size of some existing conductors should take place, although this is not allowed by the utilities because of practical reasons such as load growth, variations in loading scenarios, and the possibility of connecting DG units with uncertain penetration levels and locations. Hence, a practical feeder reinforcement approach is proposed to maintain the constraints while considering these uncertainties. Further, a novel feeder reinforcement index is proposed to assist the distribution system operators and planners to determine the feeders that first need to be reinforced. The results obtained show that the proposed reinforcement approach attains a better level of HC than can be obtained with the conventional conductor selection approach under the same testing conditions.

With the large-scale penetration of demand-side distributed generations (DG), the conventional low-voltage distribution network is becoming increasingly complex in the terms of synchronization stability and control. This paper presents the evolution process of energy transfer topology in the mathematical model, analyzes the network model and the synchronization stability of large-scale DGs in a low-voltage active distribution network. Topological mathematical models are established as the object to research incorporate DGs into three networks (i.e., star-shaped, circle-shaped, and tree-shaped networks) without changing the network architecture. Based on the Kuramoto oscillator form from a complex network theory perspective, the large-scale DGs with the frequency-droop controllers in the network can be transformed into a generalized Kuramoto model. Accordingly, by comparing the above proposed models with the standard networks (i.e., fully coupled network, NW small world network, and BA scale-free network), we discuss the synchronization stability for different topology structures of an active distributed network. The effectiveness and the superiority of the proposed topology structure are further demonstrated through numerical simulation methods, including frequency stability, phase stability, order parameters, and spectrum analysis. Furthermore, the improvement of Iceland 189-node grid is employed to prove the better stability with the star-shaped connection.

As Internet-of-Things (IoT) technology emerges, smart manufacturing has recently attracted a large amount of attention. Smart manufacturing leads to smart energy management because of its significant operating expenditure savings. However, it is believed that centralized energy management of IoT devices will impose critically large overhead since massive numbers of IoT devices are expected to be deployed. Therefore, distributed energy management or demand response (DR) is deemed to be a better solution for emerging massive smart manufacturing systems. There have been a significant number of distributed DR algorithms, including Stackelberg game theoretic approaches. However, the Stackelberg game theoretic approaches require a large number of iterations to reach Nash equilibrium, which in turn necessitates communication overheads among IoT devices. This communication overhead causes a large amount of energy consumption as well as delay. In this paper, we propose a light-weight DR scheme based on the Stackelberg model without iterations for the massive smart manufacturing systems. The proposed scheme manages energy consumption based on a non-iterative Stackelberg model and historical real-time pricing. To the best of our knowledge, our approach is the first technique that considers communication overheads for the DR technique. The performance evaluation demonstrates that the proposed scheme shifts operations to avoid peak loads, and the electricity bill is significantly reduced, operations occur at preferred times, and communication energy consumption and delay are minimized.

With the proliferation of renewable energy, the uncertainty has challenged the continuous operation of microgrids; thus, it is of importance to tackle uncertainties in power system operation. In this paper, a global sensitivity analysis (GSA) method is proposed to evaluate the influence of uncertainties on the power flow of islanded microgrids (IMGs). First, a probabilistic power flow model for IMGs is established considering the droop-controlled distributed generation units and the uncertainties of renewable energy generation output and load demands. Then, the global sensitivity analysis is introduced to identify important variables that affect IMG power flow. In addition to conventional GSA indices, the Shapley value-based GSA index is designed to evaluate the influence of correlated input variables. Moreover, the sparse polynomial chaos expansion is used to establish the surrogate models of IMG power flow, which improves the efficiency of GSA. Finally, the proposed method is tested on the 33-bus and 69-bus IMG systems, and the simulation results are compared with those considering other methods. The rankings of random input variables that affect IMG power flow are given, and the influence of correlation between different variables is discussed.

Power generated by the natural gas (NG) is a promising option for solving the restrictions on the development of the power industry. Consequently, the high interdependence between NG network and electricity network should be considered in this integration. In this paper, a day-ahead scheduling framework of integrated electricity and NG system (IENG) is proposed at a distribution level based on the fast alternating direction multiplier method with restart algorithm considering demand side response and uncertainties. Within the proposed framework, the detailed model of the IENG system at a distribution level is established, where the NG flow equation is processed by incremental linearization method to improve the computational efficiency. The objective is to minimize the operation costs of the entire system. With consideration of the uncertainties of distributed generation and electricity load as well as the uncertainties from the NG load, a two-stage robust optimization model is introduced to obtain the worst case within the uncertainty set, which is solved by column and constraints generation algorithm. In addition, the demand-side response (DSR) model including the decentralized air conditioning (AC) load model and the centralized ice-storage AC load model is integrated into the scheduling framework. Finally, the proposed day-ahead scheduling framework is verified by numerical studies where the optimal scheduling schemes are obtained in different cases, both the effects of the uncertainties and the performance with introducing DSR to the system operation are analyzed. Published version

A distribution network is one of the main parts of a power system as it is connected directly to the load center and can act as a microgrid during an islanding operation. The concept of integrating both renewable and distributed energy sources at the distribution level is currently of great interest for power system engineers. Further research by power companies and engineers on applying better techniques to improve the power quality and stability of dynamic devices in a distribution network is ongoing. This paper presents a review of fundamental distribution network architectures, including radial, ring, and mesh ones, which considers their types of operation, control and management, growth model, and advantages and disadvantages. The existing networks suffer from various technical and quality issues while they are also vulnerable to the natural disaster and any type of fault in the system. To overcome the problems associated with present networks, a novel architecture of aromatic structure is proposed. Simulations carried out show that this new network can perform better than existing ones in terms of load flow, power quality, reliability, and islanding operations. It is also a perfect choice for both a distribution network and microgrid in a cyclone-prone area as it has both overhead and underground network facilities.

This paper proposes a distributed optimization algorithm for scheduling the energy consumption of multiple smart homes with distributed energy resources. In the proposed approach, the centralized optimization problem for home energy management is decomposed into a two-level optimization problem, corresponding to the local home energy management system (LHEMS) at the first level and the global home energy management system (GHEMS) at the second level. The controllable household appliances (e.g., air conditioner and washing machine) are scheduled in the LHEMS within the consumer’s preferred appliance scheduling and comfort level, while the energy storage system and power trading between households are scheduled in the GHEMS. In the simulation study, the proposed distributed algorithm shows almost equivalent performance to the centralized algorithm in terms of the electricity cost and the consumer’s comfort level. The impact of different network topologies on the proposed algorithm is also analyzed, and the result provides insight into the selection of the optimal network configuration in view of the consumer’s electricity cost saving.

As the electricity demand continues to grow and renewable energy resources are incorporated into the grid, an effective dynamic state estimation is required for monitoring and grid synchronization. This paper proposes a microgrid state estimation approach considering the unreliable communication channels. Particularly, the renewable microgrid incorporating distributed energy resources, such as solar panels, are represented as a state-space linear model. Then, the wireless sensor network is adopted to sense the microgrid states. For long-distance transmission, we propose an innovative smart grid infrastructure, which is easy to design and offers a reliable two-way communication. In this infrastructure, the modulated signal is transmitted over an unreliable channel, which causes signal distortions. After demodulation and dequantization, the received signal at the energy management system is used the fading Kalman filter algorithm. Using the tunable forgetting factor in the predicted error covariance step, this algorithm aims to minimize the estimation errors so the estimated states reflect the true system states. Simulations results indicate that the developed approach estimates the system states within 0.25 seconds.

Compared with line powered wireless sensor nodes, power allocation, and rate adaption in energy harvesting (EH) nodes are challenging problems, due to the intermittent nature of the energy arrival process and data causality constraints. To address this problem, in this paper we propose an online fuzzy logic-based power allocation (FPA) scheme for a cooperative and delay constraint EH wireless sensor network (EHWSN) using opportunistic relaying. An EHWSN model is developed with finite energy and data buffer constraints at the relay node. The performance of FPA scheme is evaluated in terms of throughput, fairness, delay, transmission time, and energy consumption. The performance of FPA is also compared with one offline policy and two online policies. It is observed that the delay and the energy consumption of FPA scheme for a given throughput are very close to the offline power allocation policy. The results show that for a given EH profile tradeoff exists between data buffer capacity and throughput for a given payload in all the schemes. In addition, the opportunistic behavior of relay can be controlled by increasing buffer size, however, that may compromise the throughput beyond a certain buffer size. Furthermore, the performance of FPA scheme is also evaluated under Uniform, Rayleigh and Beta distributed energy arrivals.

This paper presents a fuzzy adaptive model predictive approach for load frequency control of an isolated micro grid. A generalized state space model of a typical isolated micro grid having controllable and uncontrollable generating power sources is derived, and the same has been utilized to predict the future output and control inputs for the micro grid frequency control. A centralized model predictive control (MPC) is implemented with a single input multi-output system model based on the controllable distributed energy resources in the micro grid. The parameter-driven MPC is made adaptable by dynamically adjusting its parameter $R_{w}$ using fuzzy controller. The proposed fuzzy MPC employs a rule-based fuzzy controller to fuzzify the tuning parameter $R_{w}$ present in the cost function of MPC, which plays an important role in minimizing the frequency deviation in the system. The closed loop system response obtained by the proposed fuzzy MPC has been found faster and adaptable for different scenarios in the system. The effectiveness has been evaluated with performance index integral time square error (ITSE) value and has been compared with MPC with constant tuning parameter value also with the proportional–integral controller response. Thus, the efficacy of using proposed fuzzy MPC in secondary load frequency control has been validated thereof.

Optimal allocation of distributed generation units is essential to ensure power loss minimization, while meeting the real and reactive power demands in a distribution network. This paper proposes a solution to this non-convex, discrete problem by using the hybrid grey wolf optimizer, a new metaheuristic algorithm. This algorithm is applied to IEEE 33-, IEEE 69-, and Indian 85-bus radial distribution systems to minimize the power loss. The results show that there is a considerable reduction in the power loss and an enhancement of the voltage profile of the buses across the network. Comparisons show that the proposed method outperforms all other metaheuristic methods, and matches the best results by other methods, including exhaustive search, suggesting that the solution obtained is a global optimum. Furthermore, unlike for most other metaheuristic methods, this is achieved with no tuning of the algorithm on the part of the user, except for the specification of the population size.

The widespread installation of distributed energy resources and demand side management resources into the distribution system has the potential to lead its operational parameters outside of their allowable limits. A standard strategy would be to increase the capacity of the network, however, this is costly. A cost-effective strategy could be to improve the management of such a system by improving the determination of its actual capacity. Recent advances in ICT equipment make it appear possible to manage the critical loading conditions instead of increasing capacity of the network. Thus, an energy management system is required to perform that function during the critical loading. In this paper, the authors present an approach for controlling network-oriented energy management systems on a distribution network using network capacity constraints. The proposed approach has been formulated in terms of linear inequality constraints. These constraints are derived from the functional relationships between an arbitrary set of control variables and an extensive variety of constrainable operational variables. The feasibility of the proposed constrained generation is demonstrated using linear programming, which performs the state optimization that already permits the usage of an extensive variety of cost functions. The significant feature of this approach is that it is flexible in terms of both constraint parameters and control variables. The proposed approach is demonstrated using an IEEE 13 bus distribution system.

The emergence of distributed energy resources can lead to congestion in distribution grids. DC distribution grids are becoming more relevant as more sources and loads connected to the low voltage grid use dc. Bipolar dc distribution grids with asymmetric loading can experience partial congestion resulting in a nodal price difference between the two polarities if a respective market model is applied. In order to take into account this price difference, this paper presents an optimal power flow (OPF) model formulated in terms of voltage and current. In the case of bipolar dc distribution grids, the single line approximation is no longer valid because current can flow in the neutral conductors as well. Moreover, loads and sources can be connected between any two nodes in the network. The proposed exact OPF formulation includes bilinear equations. The locational marginal prices (LMP) are derived by linearizing the problem at the optimal solution. Example cases show the various phenomena that can appear under asymmetric loading, such as pole-to-pole connections combined with pole-to-neutral connections, parallel sources, meshed grids and their effect on the LMP.

International audience; Powering cellular base stations with renewable energy are one of the long-term strategies for achieving green networks and reducing their operational costs. As an energy provider, the power grid is evolving into a smarter one, which allows more energy-efficient cellular networks and enables cooperation and interaction with the smart grid. On one hand, cellular networks can use harvested renewable energy and on-site energy storage to reduce their energy costs. On the other hand, the price of electricity depends on the energy load, which will eventually contribute to decreasing the peak consumption and global energy cost. In this paper, we propose new integration architecture for renewable energy-powered cellular networks and the smart grid. The proposed architecture is designed based on the classification and the analysis of the existing proposals and the requirements of the smart grid, renewable energy systems, and cellular networks.

© 2013 IEEE. Recently, the renewable distributed energy resources (DERs) have become more and more popular due to carbon-free energy sources and environment-friendly electricity generation. Unfortunately, these power generation patterns are mostly intermittent in nature and distributed over the electrical grid, which creates challenging problems in the reliability of the smart grid. Thus, the smart grid has a strong requisite for an efficient communication infrastructure to facilitate estimating the DER states. In contrast to the traditional methods of centralized state estimation (SE), we propose a distributed approach to microgrid SE based on the concatenated coding structure. In this framework, the DER state is treated as a dynamic outer code, and the recursive systematic convolutional (RSC) code is seen as a concatenated inner code for protection and redundancy in the system states. Furthermore, in order to properly monitor the intermittent energy source from any place, this paper proposes a distributed SE method. Particularly, the outputs of the local SE are treated as measurements, which are fed into the master fusion station. At the end, the global SE can be obtained by combining local SEs with corresponding weighting factors. The weighting factors can be calculated by inspiring the covariance intersection method. The simulation results show that the proposed method is able to estimate the system state properly.

To further liberalize the retail electricity market, this paper establishes a novel active distribution system market (ADSM) to encourage the new entries of micro virtual power plants ( $\mu $ VPPs) as prosumers. $\mu $ VPPs compete with traditional retailers by submitting price–quantity bids/offers for energy and reserve resources. The joint operation of energy and reserve market is modeled as a bilevel equilibrium problem with equilibrium constraints (EPEC) with an upper-level objective that maximizes all $\mu $ VPPs’ profit and a lower level objective that maximizes social welfare of the market clearing process. A coevolutionary approach is successfully employed to determine the pure strategy Nash equilibrium of the EPEC model. The case studies demonstrate the effectiveness of the coevolutionary method and show that $\mu $ VPPs’ bidding/offering strategies depend significantly on the penetration level of distributed energy resources and renewable energy sources and they can be considerably influenced by rivals’ strategies. This paper then compares $\mu $ VPPs’ performances under different market structures and addresses the advantages of the proposed ADSM in terms of higher asset utilization rate, higher economic profit, and more secured return on investment.

The number of electric vehicles (EVs) and the size of smart grid are witnessing rapid expansion in both spatial and temporal dimensions. This requires an efficient dynamic spatio-temporal allocation strategy of charging stations (CSs). Such an allocation strategy should provide acceptable charging services at different deployment stages while meeting financial and technical constraints. As new CSs get allocated, distributed generation (DG) units need to be also dynamically allocated in both space and time to compensate for the increment in the loads due to the EV charging requests. Unfortunately, existing power grid models are not suitable to reflect such spatio-temporal evolution, and hence, new models need to be developed. In this paper, we propose a spatio-temporal expanding power grid model based on stochastic geometry. Using this flexible model, we perform a dynamic joint allocation of EV CSs and DG units based on a constrained Markov decision process. The proposed dynamic allocation strategy accounts for charging coordination mechanism within each CS, which in turn allows for maximal usage of deployed chargers. We validate the proposed stochastic geometry-based power grid model against IEEE 123-bus test system. Then, we present a case study for a 5-year CSs deployment plan.

In this paper, the problem of distributed event-based control of large scale power systems in presence of denial-of-service (DoS) cyber attacks is addressed. Towards this end, a direct current (DC) microgrid composed of multiple interconnected distributed generation units (DGUs) is considered. Voltage stability is guaranteed by utilizing decentralized local controllers for each DGU. A distributed discrete-time event-triggered (ET) consensus-based control strategy is then designed for current sharing in the DGUs. Through this mechanism, transmissions occur while a specified event is triggered to prevent unessential utilization of communication resources. The asymptotic stability of the ET-based controller is shown formally by using Lyapunov stability via linear matrix inequality (LMI) conditions. The behavior of the DGUs subject to DoS cyber attacks are also investigated and sufficient conditions for secure current sharing are obtained. Towards this end, a switching framework is considered between the communication and attack intervals in order to derive sufficient conditions on frequency and duration of DoS cyber attacks to reach the secure current sharing. The validity and capabilities of the presented approach is confirmed through a simulation case study.

In electrical distribution grids, the constantly increasing number of power generation devices based on renewables demands a transition from a centralized to a distributed generation paradigm. In fact, power injection from distributed energy resources (DERs) can be selectively controlled to achieve other objectives beyond supporting loads, such as the minimization of the power losses along the distribution lines and the subsequent increase of the grid hosting capacity. However, these technical achievements are only possible if alongside electrical optimization schemes, a suitable market model is set up to promote cooperation from the end users. In contrast with the existing literature, where energy trading and electrical optimization of the grid are often treated separately, or the trading strategy is tailored to a specific electrical optimization objective, in this paper, we consider their joint optimization. We also allow for a modular approach, where the market model can support any smart grid optimization goal. Specifically, we present a multi-objective optimization problem accounting for energy trading, where: 1) DERs try to maximize their profit, resulting from selling their surplus energy; 2) the loads try to minimize their expense; and 3) the main power supplier aims at maximizing the electrical grid efficiency through a suitable discount policy. This optimization problem is proved to be non-convex, and an equivalent convex formulation is derived. Centralized solutions are discussed and a procedure to distribute the solution is proposed. Numerical results to demonstrate the effectiveness of the so obtained optimal policies are finally presented, showing the proposed model results in economic benefits for all the users (generators and loads) and in an increased electrical efficiency for the grid.

This paper presents the field validation of a method that performs steady-state model synthesis (SSMS) of active distribution networks using syncrophasor measurements. The validation is performed by applying the SSMS method on a real active distribution feeder network by utilizing the measurements from real phasor measurement units (PMUs) installed at the EPFL campus. An extended version of total vector error and a power flow comparison at the PMU buses are used as performance assessment metrics. A real-time hardware-in-the-loop simulations set up at the Distributed Energy System Laboratory is used for further performance assessment of the SSMS application. The effectiveness of the SSMS application is demonstrated by testing it extensively for several different case studies.