Search

Voltage rise caused by high levels of distributed generation is manifesting as voltage regulation challenges for many electricity network service providers. In this environment it would be ideal to reduce supply voltage magnitudes, however, many network operators are hesitant to do so due to concerns related to consumer appliance performance at reduced supply voltage magnitudes. Voltage regulation requirements are defined by network standards and network service providers must ensure voltages remain within specified limits. Through an evaluation of domestic appliance performance when supplied at various voltage magnitudes, this paper examines the impact of varying voltage levels on residential appliances. Equipment energy demand, operation and actuation were monitored for each applied voltage magnitude. While no equipment failures were recorded, appliance behaviour varied significantly with applied voltage magnitude. Individual appliance conservation voltage reduction (CVR) factors have also been established. The results highlight the importance of good voltage regulation and provide substantiated appliance performance figures for future studies. The outcomes of this paper allow electricity network service providers to understand the implications of supply voltage magnitude on domestic appliance performance, whether it be understating of the impact of higher voltage magnitudes caused by distributed generation or implications of reducing voltage magnitudes to provide headroom for distributed generation integration. [ABSTRACT FROM AUTHOR]

This paper studies the impact of stochastic load variations on distributed optimal load tracking and allocation (OLTA) problems in cyber-physical DC microgrids (MGs) for transportation electrification. Without load variations, the distributed optimization strategies developed in our earlier work can achieve convergence to global optimal solutions in a multi-objective optimization that balances fair load allocation and power loss reduction. Under persistent stochastic load variations, this paper develops distributed optimal strategies to track time-varying loads under noisy observations and establishes their convergence properties and error bounds. The limiting behavior of the errors characterizes the fundamental impact of the step size on irreducible errors due to conflict between attenuating observation noises and tracking load changes. Optimality conditions and algorithms for selecting the optimal step size are introduced to guide step size selection in practical applications. Simulation studies on real-world systems demonstrate the effectiveness of the proposed algorithms and validate the theoretical results. [ABSTRACT FROM AUTHOR]

The availability of high-fidelity energy networks brings significant value to academic and commercial research. However, such releases also raise fundamental concerns related to privacy and security as they can reveal sensitive commercial information and expose system vulnerabilities. This paper investigates how to release the data for power networks where the parameters of transmission lines and transformers are obfuscated. It does so by using the framework of Differential Privacy (DP), that provides strong privacy guarantees and has attracted significant attention in recent years. Unfortunately, simple DP mechanisms often result in AC-infeasible networks. To address these concerns, this paper presents a novel differentially private mechanism that guarantees AC-feasibility and largely preserves the fidelity of the obfuscated power network. Experimental results also show that the obfuscation significantly reduces the potential damage of an attack carried by exploiting the released dataset. [ABSTRACT FROM AUTHOR]

The second part of this two-paper series analyzes the incorporation of demand response (DR) and energy storage systems (ESSs) in the joint distribution and generation expansion planning for the isolated system of La Graciosa, Canary Islands, Spain. Based on the stochastic programming model developed in its companion paper, the impact of DR and ESS on the location and size of new generation and storage units and the distribution assets to be installed, reinforced, or replaced are defined. Numerical results illustrate the effective performance of the proposed approach. Additionally, a set of metrics showing the welfare achieved by the different stakeholders has been implemented. [ABSTRACT FROM PUBLISHER]

This paper introduces a new computational framework to account for uncertainties in day-ahead electricity market clearing process in the presence of demand response providers. A central challenge when dealing with many demand response providers is the uncertainty of its realization. In this paper, a new economic dispatch framework that is based on the recent theoretical development of the scenario approach is introduced. By removing samples from a finite uncertainty set, this approach improves dispatch performance while guaranteeing a quantifiable risk level with respect to the probability of violating the constraints. The theoretical bound on the level of risk is shown to be a function of the number of scenarios removed. This is appealing to the system operator for the following reasons: 1) the improvement of performance comes at the cost of a quantifiable level of violation probability in the constraints and 2) the violation upper bound does not depend on the probability distribution assumption of the uncertainty in demand response. Numerical simulations on: 1) 3-bus; 2) IEEE 14-bus system; and 3) IEEE 118-bus system suggest that this approach could be a promising alternative in future electricity markets with multiple demand response providers. [ABSTRACT FROM AUTHOR]

Demand response (DR) has been widely recognized as an important approach to balance the power grid and reduce peak load of power systems. In order to better estimate the capability and the expense of peak load reduction through DR, we need to obtain the residential load profile and customers’ attitudes toward DR programs. Based on a large-scale online survey collected among over 1500 customers from New York and Texas in the U.S., this paper investigates the relationships among household appliance activities (e.g., electric water heater and air conditioner), load profiles, and incentive-based DR (IBDR) participation for peak load curtailment through reward payment. The daily load profiles of major home appliances are developed. Additionally, this paper estimates the expense of reducing the yearly peak of the local grid load. Finally, this paper addresses the importance of investigating the multifaceted factors of affecting IBDR participation and provides useful suggestions to utility companies when implementing DR programs. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent modeling and optimization method to set up the smart energy hub model for the multiple energy system and optimize the operation of its associated energy hub model automatically. The core idea of the proposed method is to divide the complex energy hub model into several simple energy hub models. In this paper, the proposed computerized algorithm, which is applied to the smart energy hub model division, includes an energy loop intelligent searching method, a real nodes arrangement method, and a virtual nodes insertion method. One case study for the multiple energy system of a residential district in Guangzhou city is performed to validate the effectiveness of the proposed method. Compared with the traditional calculation method, the proposed method guarantees global optimal operation decision for the whole multiple energy system and the computational burden is significantly reduced. [ABSTRACT FROM AUTHOR]

The eleven papers in this special section focus on the industrial and commercial potential of demand response (DR). Customers from this non-residential market base have great potential in providing flexibility for power systems through diverse demand response (DR) programs. Intelligent energy management can be carried out with DR in industrial and commercial facilities, especially if onsite control, information, and communication technologies are available, enabling also the inherent automation capabilities of heating, ventilation, and air conditioning systems. In the dawn of the Smart Grid era, with increasing distributed generation and the conversion of traditionally passive consumers to newly active energy players in the market, DR is being effectively considered for outage management and network reinforcement deferral. [ABSTRACT FROM AUTHOR]

Implementing microgrids in power systems will improve the network reliability and reduce the impact of outages on end-users. Determining the most efficient boundaries of microgrids under contingencies is one of the main challenges for utilities from reliability and economics points of view. Currently, most research works have been focused on predefined boundary or static microgrids regardless system conditions and priority or importance of customers. In this paper, a novel concept for designing and operation of flexible microgrids in order to improve the reliability of a power distribution system is proposed. Compared to current approaches, boundaries of the proposed flexible microgrids can be extended or shrunk based on generation and demand levels, technical constraints, and customers’ comfort. Furthermore, a demand response (DR) program is performed to maintain a balance between generation and consumption in the microgrid. In this paper, genetic algorithm (GA) and mixed-integer linear programming (MILP) are simultaneously applied to a model and solve two-stage optimization considering utilities’ profits and customers’ satisfaction. In planning level, GA is utilized for sitting and sizing of distributed generations and placement of switches. In operation level, MILP is used to select target switches as boundaries of optimal microgrids, model priority of customers, and determine the contribution of each load in the DR program. The case study is also presented and final results show the superiority of the proposed method compared with the traditional fixed boundaries method in microgrids. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to demand side management (DSM), using an “individualized” price policy, where each end user receives a separate electricity pricing scheme designed to incentivize demand management in order to optimally manage flexible demands. These pricing schemes have the objective of reducing the peaks in overall system demand in such a way that the average electricity price each individual user receives is non-discriminatory. It is shown in this paper that this approach has a number of advantages and benefits compared to traditional DSM approaches. The “demand aware price policy” approach outlined in this paper exploits the knowledge, or demand-awareness, obtained from advanced metering infrastructure. The presented analysis includes a detailed case study of an existing European distribution network where DSM trial data was available from the residential end-users. [ABSTRACT FROM PUBLISHER]

This paper considers the problem of user association to small-cell base stations (SBSs) in a heterogeneous and small-cell network (HetSNet). Two optimization problems are investigated, namely, maximizing the set of associated users to the SBSs (the unweighted problem) and maximizing the set of weighted associated users to the SBSs (the weighted problem), under signal-to-interference-plus-noise ratio constraints. Both problems are formulated as linear integer programs. The weighted problem is known to be NP-hard, and in this paper, the unweighted problem is proved to be NP-hard as well. Therefore, this paper develops two heuristic polynomial-time algorithms to solve both problems. The computational complexity of the proposed algorithms is evaluated and is shown to be far more efficient than the complexity of the optimal brute-force (BF) algorithm. Moreover, this paper benchmarks the performance of the proposed algorithms against the BF algorithm, the branch-and-bound CPLEX-based algorithm, and state-of-the-art algorithms, through numerical simulations. The results demonstrate the close-to-optimal performance of the proposed algorithms. They also show that the weighted problem can be solved to provide solutions that are fair between users or to balance the load among SBSs. [ABSTRACT FROM PUBLISHER]

The increasing prevalence of technologies such as advanced metering and controls and continuously variable power electronic devices are enabling a radical shift in the way frequency regulation is performed in the bulk power system. This is a welcome development in light of the increase of unpredictable and variable generation. The idea of active participation of loads in frequency markets is not new, but the rapidly changing landscape of the power grid requires new techniques for successful integration of new types of resources; this paper works towards that end. Previously, it has been shown that residential HVAC systems can be aggregated and used to provide frequency regulation by utilizing their thermal energy capacity and flexibility of energy consumption. The virtual battery model—a first-order linear dynamical model—was analytically shown to be an accurate and simple model to capture the flexibility of residential HVAC systems. This paper presents a technique for creating the same battery-type models for many other types of systems, which can be much more complex. Our technique is based on stress testing detailed software models of physical systems. A realistic case study involving the terminal building of a small airport is presented as evidence of the effectiveness of the proposed techniques. [ABSTRACT FROM PUBLISHER]

We present a simple method to calculate the marginal capacity credit of energy limited resources with increased penetration. Energy limited resources are defined as any resource with limited hours of dispatch across a day, month, or year. This includes emission limited resources, time-limited demand response, and diurnal energy storage such as batteries and pumped storage. This paper focuses on 4 h energy limited resources with daily dispatch. The method modifies well-established effective load carrying capability methodology by optimally allocating limited capacity to the highest loss of load hours. Subsequent energy limited resources are iteratively stacked upon the prior solution, creating a marginal capacity credit curve with increased penetration of energy limited resources. Two systems are tested using this method. The initial capacity credit depends on system characteristics such as load and renewable penetration. Regardless, the capacity credit for energy limited resources declines steeply with penetration. [ABSTRACT FROM AUTHOR]

Employees are increasingly using electric vehicles (EVs) as their choice of company car. Charging infrastructure is limited by undersized connection lines and a lack of charging stations on company premises. Upgrades require significant financial investment, time, and effort. Smart charging represents an approach to make the most of existing infrastructure while satisfying charging needs. The objective of smart charging depends on the business context. Objectives of interest include fair share maximization, electricity cost minimization, peak demand minimization, and load imbalance minimization. During business hours, EV arrivals and departures are predictable while still containing uncertainty. To utilize this knowledge ahead of time, this paper presents a novel approach for combining day-ahead and real-time planning for smart charging. First, we model the problem using mixed-integer programming for day-ahead planning to precompute schedules. Next, we propose a schedule guided heuristic which takes as input precomputed schedules and adapts them in real time as new information arrives. Both methods use a parameterized weighting mechanism to flexibly combine and emphasize individual objectives of smart charging. Experimental results from simulations show significant benefits of combining day-ahead and real-time planning over using a single planning approach in isolation. Improvements include increased fair share and decreased energy costs. [ABSTRACT FROM AUTHOR]

Energy hub (EH) model, which connects the input and output energy by using the coupling matrix, is an important model for multiple energy system (MES) study. In a complex MES, it is very difficult to formulate the coupling matrix directly. This paper proposes a multi-step modeling method that separates the conversion process of the EH model into several steps and set up the EH model by multiplying the coupling matrix in each step. Then, the storage location and the concept of demand response are extended and integrated in the EH model. The EH model is difficult for the optimal operation calculation due to the nonlinearity caused by dispatch factors. This paper adds variable substitution in each step to reduce the EH model’s nonlinearity. A real residential district in Guangzhou, China, and a large-scale MES are conducted to validate the efficiency of proposed modeling and calculation method. The results show that the calculation method can reduce the computational burden significantly with global optimum obtained. [ABSTRACT FROM AUTHOR]

Data centers are among the fastest growing industries in the United States economy in terms of IT service and usage of data. However, data centers consume an excessive amount of energy that contributes to huge energy costs and CO2 emissions; thus, they face challenges in energy cost savings and making a positive impact on the environment. Therefore, data centers have been striving to: 1) improve energy efficiency in server operations by applying server provisioning techniques, and 2) utilize renewable energy and energy storage for power procurement. Although data centers have obtained financial and environmental benefits from the aforementioned efforts, there still exists significant room for improvement by facilitating demand response based on the co-optimization of server operations and power procurement, which has not been well implemented in the current practice. The main objective of this paper is a new holistic approach that considers server operations and power procurement simultaneously for implementing demand response. Also, this paper develops a decision-making model based on two-stage stochastic programming to successfully address the inherent stochastic nature and demonstrate the impact of the proposed model via numerical experiments. The proposed model is designed to support practitioners’ strategic and operational decision-making for demand response in data centers. [ABSTRACT FROM AUTHOR]

The concept of demand response (DR) is relatively well-known. There is an extensive literature on possible DR system services and evaluation of regulative potential, based on statistical analyses of data concerning loads suitable for provision of the service. Some of the presented concepts have been implemented in real applications. However, as far as the authors know, this paper presents the first attempt at an implementation of a decentralized active DR system (DADR), without communication between devices, in the form of a laboratory setup consisting 1000 devices enabling primary regulation support. The proposed implementation covers an original hardware design, a software platform for flexible system development, a laboratory arrangement enabling an objective assessment of the quality of service provided by the DADR system, as well as results of a pilot test in real power system. The practical realization of the DADR system is based on an earlier elaborated and theoretically investigated control algorithm, presented in previous papers. [ABSTRACT FROM AUTHOR]

For the demand side management, the elastic power loads can be scheduled to achieve load balancing and to minimize the fluctuation of the overall load. In a process of power supply, the inelastic power loads can be regarded as a group of parameters. Then the demand response can be utilized to realize the optimal allocation of the elastic power loads. Importantly, the power load balancing can reduce the cost from power generations since no cost is spent on the requirement of frequency control. This paper focuses on the optimal allocation of the elastic load for load balancing. A load balancing problem, with the node and the group power upper bound constraints, is investigated in this paper. Water-filling algorithm is proposed for exactly and efficiently computing the optimal solutions to the power load balancing optimization problem with lower degree polynomial computational complexity. The proposed algorithm can be applied to solve the target optimization problems with large-scale due to utilization of non-derivative water-filling method. To the best of the authors’ knowledge, there is no existing algorithm reported in the open literature that can compute the exact solution to the target problem. [ABSTRACT FROM PUBLISHER]

This paper discusses a consensus-based alternating direction method of multipliers (ADMMs) approach for solving the dynamic dc optimal power flow (DC-OPF) problem with demand response in a distributed manner. In smart grid, emerging techniques together with distributed nature of data and information, significantly increase the complexity of power systems operation and stimulate the needs for distributed optimization. In this paper, the distributed DC-OPF approach solves local OPF problems of individual subsystems in parallel, which are coordinated via global consensus variables (i.e., phase angles on boundary buses of adjacent subsystems). Three distributed DC-OPF algorithms are discussed with different convergence performance and/or communication requirement. All three distributed algorithms can effectively handle prevailing constraints for the transmission network, generating units, and demand response in individual subsystems, while the global convergence can be guaranteed. In particular, based on the traditional distributed ADMM approach, a fully decentralized algorithm without the central controller is proposed in Algorithm 2 with a new communication strategy, in which only limited information on boundary buses are exchanged among adjacent subsystems. In addition, the accelerated ADMM is discussed in Algorithm 3 for improving the convergence performance. In recognizing distributed OPF approaches in literature, one major research focus on this paper is to quantify the impact of key parameters and subsystem partitioning strategies on the convergence performance and the data traffic via numerical case studies. A general guidance for subsystem partitioning is proposed and verified via large-scale power systems. [ABSTRACT FROM PUBLISHER]

Driven by recent advances in batch Reinforcement Learning (RL), this paper contributes to the application of batch RL to demand response. In contrast to conventional model-based approaches, batch RL techniques do not require a system identification step, making them more suitable for a large-scale implementation. This paper extends fitted Q-iteration, a standard batch RL technique, to the situation when a forecast of the exogenous data is provided. In general, batch RL techniques do not rely on expert knowledge about the system dynamics or the solution. However, if some expert knowledge is provided, it can be incorporated by using the proposed policy adjustment method. Finally, we tackle the challenge of finding an open-loop schedule required to participate in the day-ahead market. We propose a model-free Monte Carlo method that uses a metric based on the state-action value function or Q-function and we illustrate this method by finding the day-ahead schedule of a heat-pump thermostat. Our experiments show that batch RL techniques provide a valuable alternative to model-based controllers and that they can be used to construct both closed-loop and open-loop policies. [ABSTRACT FROM PUBLISHER]

The development of future wireless networks focuses on providing services with strict, dynamic, and diverse quality of service (QoS) requirements. In this sense, the network slicing paradigm arises as a critical piece on the efficient allocation and management of network resources, allowing for dividing the network into several logical networks with specific functionalities and performance. This paper aims at finding the best combination of access network and network slices over a heterogeneous environment to fulfill users’ requests and optimize network resources usage. We propose the Dynamic radio Access selection and Slice Allocation (DASA) algorithm, flexibly adapted to network conditions, user priorities, and mobility behavior. DASA is based on a multi-attribute decision making (MADM) and analytical hierarchy process (AHP) to face the complex problem of network selection. Moreover, it uses a cooperative game theory approach to handle load balancing during overload situations. This work presents an integral solution that combines software-defined network (SDN) and network function virtualization (NFV) technologies to improve network performance and user satisfaction. DASA algorithm is evaluated through network-level simulations, focusing on flexibility and the effective utilization of network resources during network selection and load balancing mechanisms. [ABSTRACT FROM AUTHOR]

This paper describes the application of advanced metering infrastructure data for developing energy forecasting and operational planning services in distribution networks with significant distributed energy resources. This paper describes development of three services designed for use in distribution network energy management systems. These are comprised of a demand forecasting service, an approach for constraint management in distribution networks, and a service for forecasting voltage profiles in the low voltage network. These services could be applied as part of an advanced distribution network management system in order to improve situational awareness and provide early warning of potential network issues. The methodology and its applicability is demonstrated using recorded supervisory control and data acquisition and smart meter data from an existing medium voltage distribution network. [ABSTRACT FROM PUBLISHER]

This paper presents a data-driven method for estimating annual continuous dynamic rating of power transformers to serve the long-term planning purpose. Historically, research works on dynamic rating have been focused on real-time/near-future system operations. There has been a lack of research for long-term planning oriented applications. Currently, most utility companies still rely on static rating numbers when planning power transformers for the next few years. In response, this paper proposes a novel and comprehensive method to analyze the past 5-year temperature, loading and load composition data of existing power transformers in a planning region. Based on such data and the forecasted area load composition, a future power transformer's load shape profile can be constructed by using Gaussian Mixture Model. Then according to IEEE std. C57.91-2011, a power transformer thermal aging model can be established to incorporate future loading and temperature profiles. As a result, annual continuous dynamic rating profiles under different temperature scenarios can be determined. The profiles can reflect the long-term thermal overloading risk in a much more realistic and granular way, which can significantly improve the accuracy of power transformer planning. A real utility application example in Canada has been presented to validate and demonstrate the practicality and usefulness of this method. [ABSTRACT FROM AUTHOR]

Synthetic power systems that imitate functional and statistical characteristics of the actual grid have been developed to promote researchers’ access to public system models. Developing time series to represent different operating conditions of these synthetic systems will expand the potential of synthetic power systems applications. This paper proposes a methodology to create synthetic time series of bus-level load using publicly available data. Comprehensive validation metrics are provided to assure that the quality of synthetic time series data is sufficiently realistic. This paper also includes an example application in which the methodology is used to construct load scenarios for a 10,000-bus synthetic case. [ABSTRACT FROM AUTHOR]

Fault tolerance and load balancing are two key roles in resource allocation against failures. This paper proposes a primary and backup resource allocation model with preventive recovery priority setting to minimize a weighted value of unavailable probability (W-UP) against multiple failures. W-UP considers the probability of unsuccessful recovery and the maximum unavailable probability after recovery among physical nodes. We consider that each node fails with a workload-dependent failure probability; each failure pattern occurs with a probability. The workload-dependent failure probability is a non-decreasing function revealing an empirical relationship between the workload and the failure probability for each physical node. We introduce a recovery strategy to handle the workload variation which is determined at the operation start time and can be applied for each failure pattern. Once a failure pattern occurs, the recoveries are operated according to the priority setting to promptly recover the functions hosted by failed nodes. We also discuss an approach to obtain unsuccessful recovery probability with considering the maximum number of arbitrary recoverable functions by a set of available nodes without the priority setting. We formulate the optimization problem as a mixed integer linear programming (MILP) problem. We develop a heuristic algorithm to solve larger size problems in a practical time. The developed heuristic algorithm is approximately 729 times faster than the MILP approach with 1.6% performance penalty on W-UP. The numerical results observe that the proposed model reduces W-UP compared with baselines. [ABSTRACT FROM AUTHOR]

This paper considers the cost-driven optimal energy management strategy under a complex environment, multimicrogrids system. To provide the flexibility of load in depth, the heating, ventilating, and air conditioning (HVAC) load is investigated and explicitly formulated incorporated with indoor temperature dynamic function. The operation cost of wind generation includes the basic cost and additional cost to deal with the uncertainty of wind generation, which provides the tradeoff between optimality and possibility. Furthermore, the energy management problem of multimicrogrid is presented according to different characteristics of generation devices, storage devices, and load. A distributed neurodynamic algorithm is presented to solve the nonsmooth optimization of energy management of multimicrogrids system. By this method, the only information exchanged among microgrids is the intermediate variable when computing. The simulation results validate the effectiveness of the proposed cost-driven energy management strategy. [ABSTRACT FROM AUTHOR]

This paper critically assesses the issue of revenue inadequacy due to the presence of standalone demand response providers (DRPs) in day-ahead wholesale electricity markets. Given the increasing penetration of renewable resources such as wind and solar, demand response offers a potential tool for mitigating their uncertainties. We find that if standalone DRPs are treated as “virtual generators,” there will be revenue inadequacy in the wholesale electricity market. In this paper, through a conceptual example we examine the economic model of DRPs in the wholesale electricity market. A potential two-step solution to the revenue inadequacy problem is proposed, which comprises of modified locational marginal prices and optional transfer payments between the load serving entities and DRPs. Two simple numerical examples are provided with the purpose of illustrating our proposed approach for the design of electricity markets with standalone DRPs. [ABSTRACT FROM AUTHOR]

With the development of deregulated electricity markets, a customer can enter a contract with one of several competing utility companies. Meanwhile, a utility company is motivated to increase its market share by helping its customers manage their energy usage and save money through demand response programs. In this paper, we study the demand response program in deregulated electricity markets for data centers that often have significant flexibility in workload scheduling. We consider the real-time pricing and model the data centers’ coupled decisions of utility company choices and workload scheduling as a many-to-one matching game with externalities. To solve such a game, we show that it admits an exact potential function, whose local minima correspond to the stable outcomes of the game. We further develop a distributed algorithm that guarantees to converge to a stable outcome. Compared with the scenario without data centers’ demand response, we show through simulation that the proposed algorithm can reduce the average contract payment of data centers by 18.7% and increase the revenue of the utility companies that offer lower electricity tariffs up to 80% by attracting more data centers as customers. [ABSTRACT FROM AUTHOR]

The issue of climate change has received considerable attention in recent decades. Therefore, renewable energies and especially wind units have become a central point of attention. The stochastic nature of wind power production is modeled by means of a scenario-based method to show the possible events in the real time. Based on the Monte-Carlo simulation method and employing constructed Rayleigh probability distribution function (PDF), several scenarios that demonstrate the behavior of wind farms in real time are generated. To this end, a uniform random variable is generated and assigned to the mentioned PDF. Afterwards, a wind speed with a probability is achieved followed by the amount of wind power generation. Also, with a scenario reduction method (forward method), the desired number of scenarios can be obtained. To cope with the uncertainties of wind power generation, resulting from the intermittent nature of this kind of energy, this paper proposes a demand response (DR)-based operation approach. In other words, unlike the previous models in the literature that considered a supplementary role for the DR, this paper introduces the main role for the DR in the operation of future electricity markets. This approach focuses on a comprehensive modeling of the DR programs (DRPs) for the operational scheduling of electricity markets, considering the uncertainties of the generation of wind turbines, aiming at increasing the network security and decreasing the operation cost. The incorporation of market-based DRPs, such as demand bidding and ancillary service DR, is also considered. Two novel quantitative indices are introduced to analyze the success of DRPs regarding efficiency and wind integration. Numerical results obtained on two IEEE test systems indicate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Small-cell architecture is widely adopted by cellular network operators to increase spectral spatial efficiency. However, this approach suffers from low spectrum temporal efficiency. When a cell becomes smaller and covers fewer users, its total traffic fluctuates significantly due to insufficient traffic aggregation and exhibits a large “peak-to-mean” ratio. As operators customarily provision spectrum for peak traffic, large traffic temporal fluctuation inevitably leads to low spectrum temporal efficiency. To address this issue, in this paper, we advocate device-to-device (D2D) load-balancing as a useful mechanism. The idea is to shift traffic from a congested cell to its adjacent under-utilized cells by leveraging inter-cell D2D communication, so that the traffic can be served without using extra spectrum, effectively improving the spectrum temporal efficiency. We provide theoretical modeling and analysis to characterize the benefit of D2D load balancing, in terms of total spectrum requirements and the corresponding cost, in terms of incurred D2D traffic overhead. We carry out empirical evaluations based on real-world 4G data traces and show that D2D load balancing can reduce the spectrum requirement by 25% as compared to the standard scenario without D2D load balancing, at the expense of negligible 0.7% D2D traffic overhead. [ABSTRACT FROM AUTHOR]

The delta-connected cascaded H-bridge converter has been applied to compensate flicker and unbalance load to meet power quality requirement. Based on the detailed power analysis, this paper presents a dc capacitor voltage-balancing technique for the converter. This paper also proposes a current limit control technique to avoid overstressing the power semiconductor devices of the converter. The control techniques in this paper are verified in a 220-V, 1-kVA laboratory test bench. [ABSTRACT FROM PUBLISHER]

Demand-side participation schemes are employed to alter customers’ use of electrical power. The design of a complete scheme comprises two separate sub-problems, a customer’s local power and energy management problem, and the task of aggregating many customers into a usable coordinated demand-side energy resource. These are usually treated separately. This paper identifies the challenges of integrating the two sub-problems and maps the space of alternative formulations and configurations. Accordingly, the subject of this paper is home energy management systems and residential demand-side aggregation (RDSA) methods, with a focus on the assumptions underpinning both and the practical requirements that an integrated aggregation system should satisfy. This paper analyzes the consequences of choosing specific scheduling frameworks, preference models and aggregation mechanisms for RDSA, and identifies the tensions that exist between various proposed model formulations and optimization methods. By elucidating the implicit tradeoffs that come with choosing an RDSA scheme’s configuration, this paper provides a guide to the most appropriate applications of the range of energy management and aggregation techniques proposed in the literature as part of an integrated scheme. [ABSTRACT FROM PUBLISHER]

The price-based demand response has been considered one of the most effective ways to reduce the peak demand of power grids. However, it is possible to form a new rebound critical peak to threaten the grid stability and economic operation when large-scale loads respond to the price signal simultaneously. Based on the Internet of Things (IoT), this paper proposes a cloud-edge coordination (CEC) automatic control strategy to enable interaction and cooperation among the power grid and massive individual air conditioners (ACs) and eliminate the grid rebound critical peak of the synchro-response. Edge computing actively provides optional cooperation electricity plans, migrates computationally intensive tasks from the cloud and guarantees the privacy of users. Considering the unreliability of network transmission, data packet dropouts (/or delays and even downtime) are inevitable and usually random in the transmission, and a dual-feedback closed-loop control is first proposed in this paper. Finally, the effectiveness of the optimized closed-loop control strategy is verified by simulation cases. [ABSTRACT FROM AUTHOR]

In this paper, we show that a group of Demand Response (DR) providing customers will benefit by transferring into a cooperative. While the benefit of aggregation is definite when unconstrained, this paper is devoted to studying benefits of a DR cooperative with a nonlinear piecewise objective function of minimizing net cost comprising energy charges and demand charges while being constrained by power system constraints and other physical constraints. We propose a two-level stochastic optimization formulation that provides a model for DR cooperatives to aggregate and schedule participating resources with the objective of minimizing the total electricity bill comprising energy and demand charges. It also provides a settlement tool for the cooperative to share costs and benefits among its members. In the first level, a stochastic mixed integer linear optimization formulation to minimize the total resource cost for the electricity market is solved considering various probabilistic scenarios to find locational marginal prices (LMPs) for 24-hours and the corresponding amount of DR scheduled for DR participants including the cooperative. In the second level, a linear optimization formulation is solved to schedule DR of cooperative members with the objective of minimizing the total electricity bill of the cooperative comprising energy and demand charges. Finally, we present a settlement tool to share the costs and benefits amongst the cooperative members, whereby all members benefit from reduced electricity bills. The proposed model was tested on modified IEEE 6-bus and 118-bus systems. The results conclusively demonstrate the benefit of the proposed cooperative model for DR where scheduling DR in concert by members has a pronounced effect in reducing demand charges for all more than that achieved by individual members minimizing their own electricity bills. [ABSTRACT FROM AUTHOR]

As an important part of incentive demand response (DR), interruptible load (IL) can achieve a rapid response and improve demand side resilience. Yet, model-based optimization algorithms concerning with IL require the explicit physical or mathematical model of the system, which makes it difficult to adapt to realistic operation conditions. In this paper, a model-free deep reinforcement learning (DRL) method with dueling deep Q network (DDQN) structure is designed to optimize the DR management of IL under the time of use (TOU) tariff and variable electricity consumption patterns. The DDQN-based automatic demand response (ADR) architecture is firstly constructed, which provides a possibility for real-time application of DR. To obtain the maximum long-term profit, the DR management problem of IL is formulated as a Markov decision process (MDP), in which the state, action, and reward function are defined, respectively. The DDQN-based DRL algorithm is applied to solve this MDP for the DR strategy with maximum cumulative reward. The simulation results validate that the proposed algorithm with DDQN overcomes the noise and instability in traditional DQN, and realizes the goal of reducing both the peak load demand and the operation costs on the premise of regulating voltage to the safe limit. [ABSTRACT FROM AUTHOR]

Manufactory load allocation can be used as an effective industrial demand response scheme to reduce operating costs for industrial multienergy microgrids (iMEMGs). In addition, combined cooling, heat, and power (CCHP) plants with auxiliary devices can provide low-cost multiple energies for industrial plants. However, uncertain power generation from renewable energy sources impairs the iMEMG's operation, leading to challenges such as increased operating costs and energy supply deficiency. To conquer these challenges, this paper proposes a multitimescale coordinated adaptive robust operation approach where manufactory load allocation and iMEMG operation are optimally coordinated on different timescales. In the weekly scheduling stage, industrial loads and CCHP units are scheduled for the following week and the hourly iMEMG operation is optimized within the week. Besides, this paper applies an adaptive robust optimization method where the uncertain renewable power generation is fully addressed. The proposed approach is tested on an iMEMG with various industrial manufactories, and it is compared with conventional methods. The simulation results indicate that compared to the conventional ones, the proposed approach can guarantee a robustly optimal operation solution for the iMEMG against any uncertainty realization. [ABSTRACT FROM AUTHOR]

Flexible demand response (DR) resources can be leveraged to accommodate the stochasticity of some distributed energy resources. This paper develops an online learning approach that continuously estimates price sensitivities of residential DR participants and produces such price signals to the DR participants that ensure a desired level of DR capacity. The proposed learning approach incorporates the dispatch decisions on DR resources into the distributionally robust chance-constrained optimal power flow (OPF) framework. This integration is shown to adequately remunerate DR resources and co-optimize the dispatch of DR and conventional generation resources. The distributionally robust chance-constrained formulation only relies on empirical data acquired over time and makes no restrictive assumptions on the underlying distribution of the demand uncertainty. The distributional robustness also allows for robustifying the otpimal solution against systematically misestimating empirically learned parameters. The effectiveness of the proposed learning approach is shown via numerical experiments. The paper is accompanied by the code and data supplement released for public use. [ABSTRACT FROM AUTHOR]

Two key traits of 5G cellular networks are much higher base station (BS) densities—especially in the case of low-power BSs—and the use of massive MIMO at these BSs. This paper explores how massive MIMO can be used to jointly maximize the offloading gains and minimize the interference challenges arising from adding small cells. We consider two interference management approaches: joint transmission (JT) with local precoding, where users are served simultaneously by multiple BSs without requiring channel state information exchanges among cooperating BSs, and resource blanking, where some macro BS resources are left blank to reduce the interference in the small cell downlink. A key advantage offered by massive MIMO is channel hardening, which enables to predict instantaneous rates a priori. This allows us to develop a unified framework, where resource allocation is cast as a network utility maximization (NUM) problem, and to demonstrate large gains in cell-edge rates based on the NUM solution. We propose an efficient dual subgradient based algorithm, which converges towards the NUM solution. A scheduling scheme is also proposed to approach the NUM solution. Simulations illustrate more than 2x rate gain for 10th percentile users vs. an optimal association without interference management. [ABSTRACT FROM PUBLISHER]

The European directive “Clean Energy for All Europeans” depicts for 2030 a new scenario for low-voltage distribution grids, where each end user will have the right to trade energy and offer demand response in the electrical market either individually or in aggregation with others. This creates a new perspective for renewable energy sources (RESs) and energy storage systems (ESSs), which become key elements to allow end users playing a substantial role in the electrical market. While many distributed RESs are already in place in low-voltage grids, the ESSs are still in infancy and will become a crucial asset of future microgrids. The optimal selection of ESS ratings and location within an energy network is still an open problem, and its solution can impact considerably the grid performance and return of investment for energy sources and distribution infrastructure. This paper presents a general methodology to select the best location and ratings of energy storage units complying with the needs of local area energy networks. The aim is to support end users and aggregators to plan their investments and operate the grid so as to attain the role and functionality envisioned by the European directive. [ABSTRACT FROM AUTHOR]

Ever-changing variables in the electricity market require energy management systems (EMSs) to make optimal real-time decisions adaptively. Demand response (DR) is the latest approach being used to accelerate the efficiency and stability of power systems. This paper proposes an hour-ahead DR algorithm for home EMSs. To deal with the uncertainty in future prices, a steady price prediction model based on artificial neural network is presented. In cooperation with forecasted future prices, multi-agent reinforcement learning is adopted to make optimal decisions for different home appliances in a decentralized manner. To verify the performance of the proposed energy management scheme, simulations are conducted with non-shiftable, shiftable, and controllable loads. Experimental results demonstrate that the proposed DR algorithm can handle energy management for multiple appliances, minimize user energy bills, and dissatisfaction costs, and help the user to significantly reduce its electricity cost compared with a benchmark without DR. [ABSTRACT FROM AUTHOR]

Demand response (DR) activation and dispatch introduce additional economic and security benefits to the power system operation and control. The strategies for DR activation and compensation are among critical market operation issues which continue to raise concerns among stakeholders. In this respect, DR cost allocation would be required to guarantee DR benefits to the independent system operator and individual participating consumers. This paper proposes a DR activation strategy considering hourly system load and locational marginal prices (LMPs) in which payoffs are optimized at effective LMP where DR dispatch occurs. This paper presents a closed-form solution for the DR activation strategy according to the FERC’s Net Benefits Test. A DR cost reallocation method is proposed using the contribution factor theory for managing financial risks which ensures market participants’ optimal payoff corresponding to the DR dispatch. The proposed framework is extended to consider practical DR activation strategies and cost reallocations. Case studies based on the IEEE 6-bus system and the IEEE 118-bus system illustrate the validity and effectiveness of the proposed DR activation strategy and cost reallocation method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel distributed solution for the operation of large populations of thermostatically controlled loads (TCLs) providing frequency support. A game-theory framework is adopted, modeling the TCLs as price-responsive rational agents that schedule their energy consumption and allocate frequency response provision in order to minimize their operational costs. The novelty of this work lies in the use of mean field games to abstract the complex interactions of large numbers of TCLs with the grid and in the introduction of an innovative market structure, envisioning distinct price signals for electricity and response. Differently from previous approaches, such prices are not designed ad hoc but are derived instead from an underlying system scheduling model. [ABSTRACT FROM AUTHOR]