In this paper, we integrate a goal network into the existing dual heuristic dynamic programming (DHP) architecture, and study its damping performance on the multi-machine power system. There are four types of neural network in our proposed design: a goal network, a critic network, an action network and a model network. The motivation of this design is to build a general mapping between the system variables and the partial derivatives of the utility function, so that these required derivatives can be directly obtained and adaptively tuned over time. However, the existing DHP design can only obtain a predefined (fixed) external utility function (or its derivatives). We apply both the proposed approach and the existing DHP approach on the multi-machine power system, and compare the damping performance on a four-machine two-area power system. The simulation results demonstrate the improved control performance with the proposed design.
In this paper, the impact of signal transmission delays on static VAR compensator (SVC) based power system damping control using reinforcement learning is investigated. The SVC is used to damp low-frequency oscillation between interconnected power systems under fault conditions, where measured signals from remote areas are first collected and then transmitted to the controller as the inputs. Inevitable signal transmission delays are introduced into such design that will degrade the dynamic performance of SVC and in the worst case, cause system instability. The adopted reinforcement learning algorithm, called goal representation heuristic dynamic programming (GrHDP), is employed to design the SVC controller. Impact of signal transmission delays on the adopted controller is investigated with fully transient model based time-domain simulation in Matlab/Simulink environment. The simulation results on a four-machine two-area benchmark system with SVC demonstrate the effectiveness of the adopted algorithm on damping control and the impact of signal transmission delays.
Growing concerns about global warming, air pollution, and fossil fuel shortages have prompted the research and development of energy efficient electric vehicles (EVs). The United States government has a goal of putting 1 million EVs on the road by 2015. The anticipated increase in EV usage, along with the use of renewable energy sources for EV charging presents opportunities as well as technical hurdles. In this work, we propose coordinated EV charging strategies for commercial charging stations in parking lots. The focus of the research is on minimizing energy drawn from the grid while utilizing maximum energy from renewable energy resources in order to maximize benefits to parking lot owners. We propose an optimal control theory based strategy for EV charging. Specifically we derive a centralized iterative control approach in which the charging rates of EVs are optimized one at a time. Through analysis and simulations, we demonstrate that optimizing the charging rate of one vehicle at a time and repeating this process for all vehicles iteratively converges to the global optimum.
Partial shading is one of the important issues in maximum power point (MPP) tracking (MPPT) for photovoltaic (PV) systems. Multiple peaks on the power-voltage (P-V) curve under partial shading conditions can result in a conventional MPPT technique failing to track the global MPP, thus causing large power losses. Whereas, evolutionary optimization algorithms exhibit many advantages when applying them to MPPT, such as, the ability to track the global MPP, no requirement for irradiance or temperature sensors, system independence without knowledge of the PV system in advance, reduced current/voltage sensors compared to conventional methods when applied to PV systems with a distributed MPPT structure. This paper presents a uniform scheme for implementing evolutionary algorithms into the MPPT under various PV array structures. The effectiveness of the proposed method is verified both by simulations and experimental setup. The implementation of the ant colony optimization (ACO) based MPPT is conducted using this uniform scheme. In addition, a strategy to accelerate the convergence speed, which is important in systems with partial shading caused by rapid irradiance change, is also discussed.
Electric vehicles are a key prospect for future transportation. A large penetration of electric vehicles has the potential to reduce the global fossil fuel consumption and hence the greenhouse gas emissions and air pollution. However, the additional stochastic loads imposed by plug-in electric vehicles will possibly introduce significant changes to existing load profiles. In his paper, electric vehicles loads are integrated into an 5-unit system using a non-convex dynamic dispatch model. The actual infrastructure characteristics including valve-point effects, load balance constrains and transmission loss have been included in the model. Multiple load profiles are comparatively studied and compared in terms of economic and environmental impacts in order o identify patterns to charge properly. The study as expected shows ha off-peak charging is the best scenario with respect to using less fuels and producing less emissions.
Short-term prediction of meteorological variables is important for many applications. For example, many `smart grid' planning and control scenarios rely on accurate short term prediction of renewable energy generation, which in turn requires accurate forecasts of wind-speed, cloud-cover, and other such variables. Accurate short-term weather forecasting therefore enables smooth integration of renewables into future intelligent power systems. Weather forecasting at a specific location is currently achieved by numerical weather prediction (NWP), or by statistical models built from local time series data, or by a hybrid of these two methods broadly known as `downscaling'. We introduce a new data-intensive approach to localized short-term weather prediction that relies on harvesting multiple freely available observations and forecasts pertaining to the wider geographic region. Our hypothesis is that NWP-based forecast resources, despite the benefit of a dynamical physics-based model, tend to be only sparsely informed by observation-based inputs at a local level, while statistical downscaling models, though locally well-informed, invariably miss the opportunity to include rich additional data sources concerning the wider local region. By harvesting the data stream of multiple forecasts and observations from the wider local region we expect to achieve better accuracy than available otherwise. We describe the approach and demonstrate results for three locations, focusing on the 1hr-24hrs ahead forecasting of variables crucial for renewables forecasting. This work is part of the ORIGIN EU FP7 project (www.origin-concept.eu) and the weather forecasting approach, used in ORIGIN as input for both demand and renewables prediction, began live operation (initially for three European locations) in October 2014.
A mini-grid with various distributed energy technologies such as micro-turbine, micro-hydro, wind, solar, and biomass is known as microgrid. A microgrid can either be connected to the main grid or operate stand-alone. Due to variable nature of renewable resources such wind and solar plants, energy storage becomes necessary to maintain reliability of power supply to critical loads if high level of wind and solar power penetration is to be maximized. Moreover, advanced energy management systems are critical to make intelligent decisions that minimize power outage to critical loads, and maximize the utilization of renewable sources of energy. The primary contribution of this paper is to investigate the impact of size and state of charge (SOC) of a battery energy storage system (BESS) for a given microgrid with dynamic energy management systems (DEMS). Results are presented to show the relative performance of two types of DEMS for a microgrid with different BESS size and initial SOC. The performance of an intelligent DEMS developed using an adaptive critic designs approach is compared with of a DEMS developed using a decision tree based approach.
Disaggregating total household's energy data down to individual appliances via non-intrusive appliance load monitoring (NALM) has generated renewed interest with ongoing or planned large-scale smart meter deployments worldwide. Of special interest are NALM algorithms that are of low complexity and operate in near real time, supporting emerging applications such as in-home displays, remote appliance scheduling and home automation, and use low sampling rates data from commercial smart meters. NALM methods, based on Hidden Markov Model (HMM) and its variations, have become the state of the art due to their high performance, but suffer from high computational cost. In this paper, we develop an alternative approach based on support vector machine (SVM) and k-means, where k-means is used to reduce the SVM training set size by identifying only the representative subset of the original dataset for the SVM training. The resulting scheme outperforms individual k-means and SVM classifiers and shows competitive performance to the state-of-the-art HMM-based NALM method with up to 45 times lower execution time (including training and testing).
Future power distribution systems are expected to have large number of scale smart measuring devices and distributed generation (DG) units which would require real-time network management. Integration of single-phase DG and advanced metering infrastructure (AMI) technologies will add further complexity to the power distribution system which is inherently unbalanced. In order to alleviate the negative impacts associated with the integration of DG, transformation from passive to active control methods is imperative. If properly regulated, DGs could provide voltage and reactive power support and mitigate the volt/VAr problem. This paper presents a distributed algorithm to provide voltage and reactive power support and minimize power losses in unbalanced power distribution systems. Three-phase volt/VAr control problem is formulated and active/reactive powers of DGs are determined in a distributed fashion by decomposing the overall power distribution system into zonal sub-systems. The performance is validated by applying the proposed method to the modified IEEE 37 node test feeder.
The wind turbine power curve (WTPC) shows the relationship between the wind speed and power output of the turbine. Power curves, which are provided by the manufacturers, are mainly used in planning, forecasting, performance monitoring and control of the wind turbines. Hence an accurate WTPC model is very important in predictive control and monitoring. This paper presents comparative analysis of various parametric and non-parametric techniques for modeling of wind turbine power curves, with reference to three commercial wind turbines; 330, 800 and 900 kW, respectively. Firstly, these WTPCs were used to evaluate the accuracy of several previously developed mathematical models by utilizing error measurement techniques such as normalized root mean square error (NRMSE) and r-square. Later on, the most accurate model was selected and the genetic algorithm (GA) was utilized to improve the model's accuracy by optimizing its coefficients. Finally, WTPCs were modeled using artificial neural network (ANN) and the result was compared with the GA optimized model.
An intensive use of dispersed energy resources is expected for future power systems, including distributed generation, especially based on renewable sources, and electric vehicles. The system operation methods and tool must be adapted to the increased complexity, especially the optimal resource scheduling problem. Therefore, the use of metaheuristics is required to obtain good solutions in a reasonable amount of time. This paper proposes two new heuristics, called naive electric vehicles charge and discharge allocation and generation tournament based on cost, developed to obtain an initial solution to be used in the energy resource scheduling methodology based on simulated annealing previously developed by the authors. The case study considers two scenarios with 1000 and 2000 electric vehicles connected in a distribution network. The proposed heuristics are compared with a deterministic approach and presenting a very small error concerning the objective function with a low execution time for the scenario with 2000 vehicles.
Communication is the heart of the Smart Grid. Smart Grid metering and control applications require fast, reliable and secured two-way communication network. In this study, random signal strength (RSS) based localization of smart meters of Advanced Metering Infrastructure (AMI) has been proposed using the location information of meters whose position are known. Based on the location information, a loop free routing protocol has been developed. As a consequence, loop freedom, local routing decision and limited flooding, faster and reliable data transmission can be achieved.
In this paper, a new method is proposed for Locational Marginal Pricing (LMP) forecasting in Smart Grid. The marginal cost is required to supply electricity to incremental loads in case where a certain node increases power demands in a balanced power system. LMP plays an important role to maintain economic efficiency in power markets in a way that electricity flows from a low-cost area to high-cost one and the transmission network congestion in alleviated. The power market players are interested in maximizing the profits and minimizing the risks through selling and buying electricity. As a result, it is of importance to obtain accurate information on electricity pricing forecasting in advance so that their desire is reflected. This paper presents the Gaussian Process (GP) technique that comes from the extension of Support Vector Machine (SVM) that hierarchical Bayesian estimation is introduced to express the model parameters as the probabilistic variables. The advantage is that the model accuracy of GP is better than others. In this paper, GP is integrated with the k-means method of clustering to improve the performance of GP. Also, this paper makes use of the Mahalanobis kernel in GP rather than the Gaussian one so that GP is generalized to approximate nonlinear systems. The proposed method is successfully applied to real data of ISO New England in USA.
Smart Grid metering and control applications require fast and secured two-way communication. IEEE 802.15.4 based ZigBee is one of the leading communication protocols for Advanced Metering Infrastructure (AMI). In North America, ZigBee supports two distinguished frequency bands - 915 MHz and 2.4 GHz. In Home Area Network (HAN) of AMI, home appliances communicate with smart meters whereas the communication among neighboring meters is termed as Neighborhood Area Network (NAN). In this study, optimum frequency bands for NAN and HAN communication have been proposed based on the throughput, reliability and scalability. We evaluated and compared the performance of bands 868/915 MHz and 2.4 GHz for AMI context. The solution also meets the requirements for Smart Grid communication standards as recommended by the US Department of Energy (DOE).
Demand response programs and models have been developed and implemented for an improved performance of electricity markets, taking full advantage of smart grids. Studying and addressing the consumers' flexibility and network operation scenarios makes possible to design improved demand response models and programs. The methodology proposed in the present paper aims to address the definition of demand response programs that consider the demand shifting between periods, regarding the occurrence of multi-period demand response events. The optimization model focuses on minimizing the network and resources operation costs for a Virtual Power Player. Quantum Particle Swarm Optimization has been used in order to obtain the solutions for the optimization model that is applied to a large set of operation scenarios. The implemented case study illustrates the use of the proposed methodology to support the decisions of the Virtual Power Player in what concerns the duration of each demand response event.
Pulsed power network is proposed for reliable and low loss electrical power distribution among various type of power sources and consumers. The proposed scheme is a derivative of power packet network so far investigated that has affinity with dispersion type power sources and has manageability of energy coloring in the process of power distribution. In addition to these advantages, the proposed scheme has system reliability and low loss property because of its intelligent operation performed by individual nodes and direct relaying by power routers. In the proposed scheme, power transmission is decomposed into a series of pulses placed at specified power slots in continuous time frames that are synchronized over the network. The power slots are pre-reserved based on information exchanges among neighboring nodes following inherent protocol of the proposed scheme. Because of this power slot reservation based on decentralized intelligence, power pulses are directly transmitted from various power sources to consumers with low power dissipation even though a partial failure occurs in the network. The network performance of the proposed scheme is simulated to confirm the protocol for the power slot reservation.
Energy fraud detection is a critical aspect of smart grid security and privacy preservation. Machine learning and data mining have been widely used by researchers for extensive intelligent analysis of data to recognize normal patterns of behavior such that deviations can be detected as anomalies. This paper discusses a novel application of a machine learning technique for examining the energy consumption data to report energy fraud using artificial neural networks and smart meter fine-grained data. Our approach achieves a higher energy fraud detection rate than similar works in this field. The proposed technique successfully identifies diverse forms of fraudulent activities resulting from unauthorized energy usage.
An important function of a Smart Grid (SG) is the Demand Side Management (DSM), which consists on controlling loads at customers side, aiming to operate the system with major efficiency and sustainability. The main advantages of this technique are (i) the decrease of demand curve's peak, that results on smoother load profile and (ii) the reduction of both operational costs and the requirement of new investments in the system. The customer can save money by using loads on schedules with lower taxes instead of schedules with higher taxes. In this context, this work proposes a simple metaheuristic to solve the problem of DSM on smart grid. The suggested approach is based on the concept of day-ahead load shifting, which implies on the exchange of the use schedules planned for the next day and aims to obtain the lowest possible cost of energy. The demand management is modeled as an optimization problem whose solution is obtained by using an Evolutionary Algorithm (EA). The experimental tests are carried out considering a smart grid with three distinct demand areas, the first with residential clients, other one with commercial clients and a third one with industrial clients, all of them possessing a major number of controllable loads of diverse types. The obtained results were significant in all three areas, pointing substantial cost reductions for the customers, mainly on the industrial area.
Option pricing models are usually described with the use of stochastic differential equations and diffusion-type partial differential equations (e.g. Black-Scholes models). In case of electric power markets these models are complemented with integral terms which describe the effects of jumps and changes in the diffusion process and which are associated with variations in the production rates, condition of the transmission and distribution system, pay-off capability, etc. Considering the latter case, that is a partial integrodifferential equation for the option's price, a new filtering method is developed for estimating option prices variations without knowledge of initial conditions. The proposed filtering method is the so-called Derivative-free nonlinear Kalman Filter and is based on a transformation of the initial option price dynamics into a state-space model of the linear canonical form. The transformation is shown to be in accordance to differential flatness theory and finally provides a model of the option price dynamics for which state estimation is possible by applying the standard Kalman Filter recursion. Based on the provided state estimate, validation of the Black-Scholes partial integrodifferential equation can be performed and the existence of inconsistent parameters in the electricity market pricing model can be concluded.
Time delays exist in most of the electronic components, digital controllers and DSPs. Certain values of time delay can easily corrupt the performance of a power control system. This time delay can strictly disturb the system dynamic in power control applications with low to medium switching frequency. In this paper, we overcome the effect of time delay in an SVPWM based switching pattern for a grid connected three-phase current source inverter. The time delay is tracked in real time and the states of the system are estimated. Our experimental results clearly show that the proposed approach can compensate the effect of the time delay and improve the quality of the performance.
