G. Papaefthymiou

Stochastic Modeling of Power Demand Due to EVs Using Copula

The driving patterns characterizing electric vehicles (EVs) are stochastic and, as a consequence, the electrical load due to EVs inherits their randomness. This paper presents a Monte Carlo procedure for the derivation of load due to EVs based on a fully stochastic method for modeling transportation patterns. Under the uncontrolled domestic charging scenario three variables are found to be crucial: the time a vehicle leaves home, the time a vehicle arrives home, and the distance traveled in between. A detailed transportation dataset is used to derive marginal cumulative distribution functions of the variables of interest. Since the variables are statistically dependent, a joint distribution function is built using a copula function. Subsequently, simulated EV trips are combined with a typical charging profile so that the energy contribution to the system is computed. The procedure is applied to analyze the effect of the EV load on the national power demand of The Netherlands under different market penetration levels and day/night electricity tariff scenarios.

Balancing wind power with virtual power plants of micro-CHPs

Higher participation levels of wind power in power systems will increase the need for flexible back-up generation to balance the differences between predicted and realized wind power production. This is often an expensive solution. With distributed energy resources and more ICT at the demand side, novel, and possibly cheaper, ways for imbalance minimization arise. Micro combined heat-and-power (micro-CHP) is a novel domestic-level generation technology, producing heat and power simultaneously. Clusters of micro-CHPs can function as flexible virtual power plants (VPPs). This paper presents the design of an online coordination scheme that can substantially reduce the imbalance volumes and the associated costs for wind power traders by actively controlling a VPP comprising micro-CHP systems. It is shown that the imbalance volume and associated cost can be reduced by 73 % and 38 %, respectively.

From transportation patterns to power demand: Stochastic modeling of uncontrolled domestic charging of electric vehicles

This paper presents a Monte Carlo simulation approach for the modeling of the power demand of electric vehicles under the scenario of uncontrolled domestic charging. A detailed transportation dataset for the Netherlands is used to derive the stochastic characteristics of the behavior of vehicles. The stochastic variables are the start/end-time of each trip and the respective travelled distance while the battery state of charge at the beginning of charging is derived by the consideration of the distance traveled since the last charging and the charging history. The stochastic variables are modeled using normal copula function based on the respective correlations and marginal distributions. The total load due to electric vehicles is computed based on the combination of the simulated commuting pattern with the charging profile of a typical electric vehicle battery. The results show that the EV power demand reaches the highest value during the evening peak hours for the residential load, however the peak is significantly lower than maximum which is mainly caused by the low charging time due to a generally low mean traveled distance.

Advantages of ARMA-GARCH wind speed time series modeling

This paper presents the advantages of using wind speed time series models from ARMA-GARCH class. The models are found using good statistical practice and are able to capture the most important characteristics of the data like distribution, time dependence structure and periodicity in a satisfying manner. It is shown that the models offer several crucial advantages. The artificial wind speeds simulated from the obtained models are statistically indistinguishable from the wind speed time series measurements recorded in other years than the original data. Moreover, models can contribute to the considerations of extreme scenarios of wind power generation by simulating wind speeds characterized by a very high energy content. Thanks to the use of continuous cdf transformations, the synthetic time series do not possess the measurement error.

Impact of Stochastic Generation in Power Systems Contingency Analysis

A new methodology is proposed for the contingency analysis of power systems with a high penetration of stochastic generation. The essence of the proposed technique is the use of a probabilistic risk measure for the security assessment of the N subsystems deriving from the application of the N-1 criterion. This measure, namely the stochastic stress of the system, corresponds to the concurrent behavior of the stochastic system inputs (loads and stochastic generators) that are situated in the lower voltage levels of the system. In the context of Monte Carlo simulation, this problem is equivalent to the sampling of a large number of non-trivial dependent random variables (stochastic power injections). The modeling procedure is split in two independent tasks: modeling the marginal distributions and modeling the stochastic dependence structure. The second part is the most cumbersome modeling problem. For this, a two-step method is used. First, clusters of positively correlated variables are defined and are modeled based on the concepts of perfect correlation (comonotonicity). Then, the exact correlations between these clusters are modeled based on the joint normal transform methodology. This powerful computational method can be easily applied to large systems with a high number of stochastic generators. The application of the method for the contingency analysis of the 5-bus/7-branch test system (Hale network) with a high penetration of wind generation is presented in the paper

Towards Faster Solution of Large Power Flow Problems

Current and future developments in the power system industry demand fast power flow solvers for larger power flow problems. The established methods are no longer viable for such problems, as they are not scalable in the problem size. In this paper, the use of Newton-Krylov power flow methods is proposed, and a multitude of preconditioning techniques for such methods are discussed and compared. It is shown that incomplete factorizations can perform very well as preconditioner, resulting in a solver that scales in the problem size. It is further shown that using a preconditioned inner-outer Krylov method has no significant advantage over applying the preconditioner directly to the outer iterations. Finally, algebraic multigrid is demonstrated as a preconditioner for Newton-Krylov power flow and argued to be the method of choice in some scenarios.

Probabilistic power flow methodology for the modeling of horizontally-operated power systems

Stochastic generation is expected to take a large share of the energy production in future power systems. Two basic features of this type of generation distinguish it from the traditional centralized, conventional generation: it is highly distributed (large number of small-scale generators) and non-dispatchable (use of an uncontrolled prime mover). The incorporation of such power sources in the lower system levels leads to a new horizontal structure of the power system, where the distribution networks contain both uncertain stochastic generation and load. For the analysis of such systems, the use of a probabilistic approach is necessary. There are two basic problems with the probabilistic formulation of this problem: the large number of random variables involved in the analysis and the presence of complex dependencies between the system inputs. In this contribution, a two-step method is presented for the stochastic modeling of the system: first, clusters of positively correlated variables are defined and modeled based on the concepts of perfect correlation (comonotonicity), and then the exact correlations between these clusters are modeled based on a new proposed technique, the joint normal transform methodology. This powerful computational method can be easily applied to large systems with a high number of stochastic generators. The proposed method has been implemented and applied for the 5-bus/7-branch test system (Hale network) with a high penetration of wind generation. The results are presented in the paper

Distributed generation vs bulk power transmission

Distributed Generation (DG) is generally considered as an alternative to bulk power transport. The basic idea is that the presence of electricity generation inside the distribution systems leads to a reduction of the local electricity needs, which consequently leads to a reduced need for power transmission capacity and thus a deferral of investments in transmission lines. However, due to the different operational characteristics of the plethora of types of distributed generation, this hypothesis may prove invalid. Controllable distributed generation, defined as local generation of which the power output can be regulated by the system operator (e.g. stand-alone gas-fired combustion units) will certainly have a positive impact on this direction. However, in reality different types of DG technologies could de implemented in the distribution systems, such as partially or stringently controlled micro-combined heat and power (micro-CHP) units operating according to different local control modes (e.g. thermal-led control) or non-controllable (stochastic) DG units, such as wind power plants. The operation of such units may lead to an opposite effect regarding the necessary transmission capacity. In this paper we first define four types of DG regarding their level of controllability. We then look into the effect on the transmission system of both stringently-controlled DG (i.e. micro-CHP) and stochastic DG (i.e. wind turbines). It is shown that micro-CHP systems may have a positive effect to the dimensioning of the transmission system, while the presence of wind power plants may instead lead to increased investment needs in power transport capacity.

Stochastic modelling and analysis of horizontally-operated power systems with a high wind energy penetration

A methodology for the modelling and analysis of horizontally-operated power systems, i.e. systems with a high penetration of stochastic renewable generation, is presented. The objective is to obtain insight in the steady-state of the transmission system when a high penetration level of stochastic distributed generation (in this study case wind power), is present in the underlying distribution systems. The results can be used for the adequacy assessment and risk management of the system. For the system stochastic modelling, the methodology proposes the decoupling of the individual (marginal) behavior of the input random variables from the dependence structure between them. The stochastic dependence is shown to be a major factor for the assessment of the aggregated effect of the distributed stochastic generation on the system. In particular, the stress in the system increases in cases of positive dependence between the inputs and the maximum stress, i.e. the worst-case scenario for the system, occurs when extreme positive dependencies are present between the inputs. Based on this modelling principle, the system operational planning and design can be performed by modelling the extreme dependencies in the system. This powerful computational method can be easily applied to large systems with a high number of stochastic generators.

Cost-benefit analysis of a coordinated grid development in the North Sea

A full Cost-Benefit Analysis (CBA) of a meshed offshore grid in the Northern Seas with respect to radial configurations has been performed. In this CBA, three scenarios have been analysed: the ENTSO-E Vision 4 scenario with 111 GW of offshore wind, the PRIMES reference scenario with 70 GW of offshore wind, and the NSCOGI scenario with 55 GW of offshore wind. The offshore grid infrastructure cost is calculated, and the benefits are assessed. The CBA is based on the ENTSO-E Draft CBA methodology. The costs were calculated using Ecofys offshore transmission cost modelling tool and Tractebel Engineerings techno-economical tool SCANNER? is used for the calculation of the benefits.