Due to the inherent uncertainty involved in renewable energy forecasting, uncertainty quantification is a key input to maintain acceptable levels of reliability and profitability in power system ...operation. A proposal is formulated and evaluated here for the case of solar power generation, when only power and meteorological measurements are available, without sky-imaging and information about cloud passages. Our empirical investigation reveals that the distribution of forecast errors do not follow any of the common parametric densities. This therefore motivates the proposal of a nonparametric approach to generate very short-term predictive densities, i.e., for lead times between a few minutes to one hour ahead, with fast frequency updates. We rely on an Extreme Learning Machine (ELM) as a fast regression model, trained in varied ways to obtain both point and quantile forecasts of solar power generation. Four probabilistic methods are implemented as benchmarks. Rival approaches are evaluated based on a number of test cases for two solar power generation sites in different climatic regions, allowing us to show that our approach results in generation of skilful and reliable probabilistic forecasts in a computationally efficient manner.
This paper presents a solar radiation forecast technique based on fuzzy and neural networks, which aims to achieve a good accuracy at different weather conditions. The accuracy of forecasted solar ...radiation will affect the power output forecast of grid-connected photovoltaic systems which is important for power system operation and planning. The future sky conditions and temperature information is obtained from National Environment Agency (NEA) and the sky and temperature information will be classified as different fuzzy sets based on fuzzy rules. By using fuzzy logic and neural network together, the forecast results can follow the real values very well under different sky and temperature conditions. The effectiveness of the approach is validated by a case study where four different scenarios are tested. The Mean Absolute Percentage Error (MAPE) is much smaller compared with that of the other solar radiation method.
•This paper presents a solar radiation forecast technique based on fuzzy logic and neural network.•The proposed technique can tell the difference of the solar radiations between the different sky conditions.•The forecast results can follow the real values very well under different weather conditions.•Compared with the other solar radiation method, the Mean Absolute Percentage Error (MAPE) is much smaller.
This paper presents a method to forecast the probability distribution function (PDF) of the generated power of PV systems based on the higher order Markov chain (HMC). Since the output power of the ...PV system is highly influenced by ambient temperature and solar irradiance, they are used as important features to classify different operating conditions of the PV system. The classification procedure is carried out by applying the pattern discovery method on the historical data of the mentioned variables. An HMC is developed based on the categorized historical data of PV power in each operating point. The 15-min ahead PDF of the PV output power is forecasted through the Gaussian mixture method (GMM) by combining several distribution functions and by using the coefficients defined based on parameters of the HMC-based model. In order to verify the proposed method, the genetic algorithm is applied to minimize a well-defined objective function to achieve the optimal GMM coefficients. Numerical tests using real data demonstrate that the forecast results follow the real probability distribution of the PV power well under different weather conditions.
Widespread adoption of electric vehicles (EVs) would significantly increase the overall electrical load demand in power distribution networks. Hence, there is a need for comprehensive planning of ...charging infrastructure in order to prevent power failures or scenarios where there is a considerable demand-supply mismatch. Accurately predicting the realistic charging demand of EVs is an essential part of the infrastructure planning. Charging demand of EVs is influenced by several factors, such as driver behavior, location of charging stations, electricity pricing, etc. In order to implement an optimal charging infrastructure, it is important to consider all the relevant factors that influence the charging demand of EVs. Several studies have modeled and simulated the charging demands of individual and groups of EVs. However, in many cases, the models do not consider factors related to the social characteristics of EV drivers. Other studies do not emphasize on economic elements. This paper aims at evaluating the effects of the above factors on EV charging demand using a simulation model. An agent-based approach using NetLogo is employed in this paper to closely mimic the human aggregate behavior and its influence on the load demand due to charging of EVs.
Spinning Reserve Estimation in Microgrids Wang, M. Q.; Gooi, H. B.
IEEE transactions on power systems,
2011-Aug., 2011-08-00, 20110801, Letnik:
26, Številka:
3
Journal Article
Recenzirano
Odprti dostop
In this paper, a probabilistic methodology for estimating spinning reserve requirement in microgrids is proposed. The spinning reserve amount is determined by a tradeoff between reliability and ...economics. The unreliability of units and uncertainties caused by load and nondispatchable units are both considered. In order to reduce computation burden, various uncertainties are aggregated. A multistep method is proposed to efficiently consider the combinatorial characteristic of unit outage events. The computation efficiency of spinning reserve calculations can be greatly improved. The optimization is solved by mixed integer linear programming (MILP). Two case studies are carried out to illustrate the proposed method. Their results and discussions are also presented.
As one of the most promising solutions to mitigate the power quality (PQ) problem of the modern power systems, the unified PQ conditioner (UPQC) draws considerable attentions. Since the UPQC consists ...of two sets of power converters, it will greatly increase the manufacturing investment of the setup. In this paper, the optimal volt-ampere (VA) ratings of the converters in the UPQC are investigated due to system compensating requirements. The phase angle control (PAC) method is discussed and illustrated to have the feature of changing the online VA loading by adjusting the corresponding displacement angle. On the basis of the variable PAC method, a two-stage algorithm is utilized to optimize the ratings of the shunt and series converters in order to obtain the maximum utilization rates of the power converters in the UPQC. Moreover, the corresponding control algorithm is utilized to reduce the proposed UPQC online VA loadings for the different compensating operations. The proposed UPQC is compared with other approaches to highlight the advantage of the proposed optimization algorithm. The proposed algorithms are also validated with the simulation and the real-time control hardware-in-loop results of the designed system.
In market operations, distributed generators (DGs) and price-sensitive loads participate in a microgrid energy market implemented in JADE. Each DG and each price-sensitive load is represented by the ...respective agents which perform various functions such as scheduling, coordination and market clearing subject to system, DG and load constraints. Each agent is assigned to one of the several agent objectives which maximizes either DG or load surpluses or both. In simulated operation of a microgrid, hourly power reference signals and load control signals from JADE are passed to DG and load models developed in MATLAB/Simulink using MACSimJX. Simulated operation of DGs and loads are studied by performing simulations under different agent objectives. Results from simulation studies demonstrate the effectiveness of implementing multi-agent system (MAS) in the distributed management of microgrids.
In this paper a mixed integer quadratic programming (MIQP) is proposed to solve the dynamic economic dispatch (DED) with valve-point effect (VPE) where the non-linear and non-smooth cost caused by ...VPE is piecewise linearized. However if the DED with VPE is directly solved by the MIQP in a single step, the optimization suffers convergence stagnancy and will run out of memory. In this paper the multi-step method, the warm start technique and the range restriction scheme are combined with the MIQP. The optimization process can then break the convergence stagnancy and the computation efficiency can be greatly improved. When the system loss is considered, the loss formula is piecewise linearized. A post-processing procedure is proposed to eliminate the approximation error caused by linearization of the loss formula. The effectiveness of the proposed method is demonstrated by seven cases and the results are compared with those obtained by the previous published methods.
•Combination of day-ahead and hour-ahead optimizations to design online controller.•Investigating the effect of load forecast error on the system operating cost.•Proposing effective method for ...hour-ahead resource re-dispatch.•Using the HSS algorithm as a powerful and effective optimization method.•Combining long-term and short-term strategies for optimal dispatch of resources.
This paper deals with a residential hybrid thermal/electrical grid-connected home energy system incorporating real data for the load demand. A day-ahead scheduling (DAS) algorithm for dispatching different resources has been developed in previous studies to determine the optimal operation scheduling for the distributed energy resources at each time interval so that the operational cost of a smart house is minimized. However, demand of houses may be changed in each hour and cannot be exactly predicted one day ahead. System complexity caused by nonlinear dynamics of the fuel cell, as a combined heat and power device, and battery charging and discharging time make it difficult to find the optimal operating point of the system by using the optimization algorithms quickly in online applications. In this paper, the demand forecast error is studied and a near-optimal dispatch strategy by using artificial neural network (ANN) is proposed for the residential energy system when the demand changes are known one hour ahead with respect to the predicted day-ahead values. The day-ahead and hour-ahead optimizations are combined and ANN training inputs are adjusted according to the problem such that the economic dispatch of different energy resources can be achieved by the proposed method compared with previous studies. Using the model of the fuel cell extracted from experimental measurement and real data for the load demand makes the results more applicable in real residential energy systems.
With the increased environmental concern, the photovoltaic (PV) generation capacity is growing in today's power systems. As the PV penetration rate increases, the intermittency and uncertainty of PV ...systems will cause frequency regulation issues. When rapid fluctuations take place, the system requires fast responding regulation to recover the frequency within a short period of time. Traditional power plants with slow dynamics are less capable of tracking the fast-changing regulation signal. In this context, a battery energy storage system (BESS) is considered as an effective regulation source to respond immediately to frequency deviations. This paper addresses the sizing issue of an aggregated BESS by a series of system level performance tests with different BESS penetration rates. The evaluation criteria are the control performance standards 1 and 2. Response effectiveness of the BESS with different levels of disturbances is also analyzed, with comparison to that of the traditional power plants. The proposed BESS aggregation controller is also validated using software simulations and a hardware testbed.
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