Variability Of Power Production Research Articles

Power Variability of Wind and Solar Production Portfolio in the Republic of Croatia

In this work, we analysed multi-annual data set of wind and solar production portfolio with different power frequency approaches and averaging methods in order to characterize power production variability at different temporal scales. All of the methods have their advantages depending on their scale and purpose, but also some shortcomings that limit their use. For the purposes of this work, we selected the method of explicit derivation as the most appropriate for the fast power change frequency analysis and characterization. 
 The variability of power production from wind and solar power plants in Croatia is strongly present on an hourly, daily and seasonal level, while on an annual level the variability is much less pronounced. Since current electricity production and consumption must remain in balance to maintain the stability of the power network, this variability of production can pose significant challenges for the inclusion of large amounts of wind and solar energy in the power system of the Republic of Croatia.
 A particular challenge for the power system in terms of production variability is the fast change in power. Fast power change affects the quality of production forecasting and consequently causes higher imbalance costs. The impact on the management and balancing of the power system is particularly challenging.

Offshore wind resource assessment by characterizing weather regimes based on self-organizing map

As offshore wind power is continuously integrated into the electric power systems in around the world, it is critical to understand its variability. Weather regimes (WRs) can provide meteorological explanations for fluctuations in wind power. Instead of relying on traditional large-scale circulation WRs, this study focuses on assessing the dependency of wind resources on WRs in the tailored region clustered based on the finer spatial scale. For this purpose, we have applied self-organizing map algorithm to cluster atmospheric circulations over the South China Sea (SCS) and characterized wind resources for the classified WRs. Results show that WRs at mesoscale can effectively capture weather systems driving wind power production variability, especially on multi-day timescale. Capacity factor reconstruction during four seasons illustrates that WRs highly influence most areas in winter and southern part of SCS in summer, and WRs can serve as a critical source of predicting the potential of wind resources. In addition, we further qualify the wind power intermittency and complementarity under different WRs, which have not been assessed associated with WRs. During WRs with changeable atmosphere conditions, the high complementarity over coastal areas can reduce the impact of intermittency on wind power generation. The proposed approach is able to be implemented in any region and may benefit wind resource evaluation and characterization.

Effective artificial neural network-based wind power generation and load demand forecasting for optimum energy management

The variability of power production from renewable energy sources (RESs) presents serious challenges in energy management (EM) and power system stability. Power forecasting plays a crucial role in optimal EM and grid security. Then, accurate power forecasting ensures optimum scheduling and EM. Therefore, this study proposes an artificial neural network- (ANN-) based paradigm to predict wind power (WP) generation and load demand, where the meteorological parameters, including wind speed, temperature, and atmospheric pressure, are fed to the model as inputs. The normalized root mean square error (NRMSE) and normalized mean absolute error (NMAE) criteria are used to evaluate the forecasting technique. The performance of ANN was compared to four machine learning methods: LASSO, decision tree (DT), regression vector machines (RVM), and kernel ridge regression (KRR). The obtained results show that ANN provides high effectiveness and accuracy for WP forecasting. Furthermore, ANN has proven to be an interesting tool in ensuring optimum scheduling and EM.

The hybrid power generation and balancing energy supply–demand for rural village in Ethiopia

AbstractEthiopia has a wide range of environmentally‐friendly, renewable energy resources that can assist the country to overcome its rural electricity problems. The Hybrid Optimization Model for Multiple Energy Resources (HOMER) software package was used to evaluate the viability of solar, hydro and wind hybrid power generation for a remote community in Ethiopia's Geba catchment. The hydrologic, climatic and demographic data were used to analyze the community's electrical supply and demand. Using survey data from chosen residences, the community's hourly and daily electricity demands were calculated. Two or more schemes were investigated besides the standalone scheme to assess the hybrid system's capacity. Wind, micro‐hydro and solar can generate an average annual power of 96.8, 272.4 and 161.8 kW, respectively. A mix of energy sources can meet the community's electricity demand. During peak demand periods, however, the hybrid system's mean hourly variability in power production showed inadequacy, which could be remedied by power stored in the battery and generator output. The economic analysis showed that the hybrid system proposed here is less expensive, indicating that hybrid energy generation might be employed to electrify Ethiopia's remote settlements. We advise decision‐makers and power authorities to use hybrid systems for rural electrification.

(Invited, Digital Presentation) Nanostructured Thin Film (NSTF) Iridium Catalyst Powder for Proton Exchange Membrane Water Electrolyzers

Proton exchange membrane water electrolyzers (PEMWEs) are electrochemical devices which generate hydrogen (H2) gas from water and electrical energy feedstocks. PEMWEs produce H2 renewably and carbon-free when the electricity is from renewable sources, and are a pathway to enable deep decarbonization across multiple industrial and energy sectors[1]. However, commercial deployment of PEMWEs is currently limited to megawatt-scale due to relatively higher H2 production costs and capital costs than hydrocarbon reforming [2]. The higher costs are due in part to the use of significant quantities of expensive materials (Pt and Ir electrocatalysts and perfluorinated ionomers), insufficient operating performance and durability, and high manufacturing costs. Additionally, commercial PEMWEs additionally use high Ir loadings [3] for the oxygen evolution reaction (OER), and the limited abundance of Ir [4] may limit PEMWE annual deployment of those technologies to gigawatt (GW) scale.3M Nanostructured Thin Film (NSTF) PEMWE OER powder catalysts and electrodes are a unique approach to address the cost and Ir utilization barriers noted above. NSTF catalysts [5] are comprised of nm-scale catalyst metal thin films on a high aspect ratio inert support (Fig. A). NSTF Ir OER catalysts enable high efficiency and high durability due to high OER mass activity and intrinsic resistance to dissolution, imparted by the unique agglomerated thin film catalyst structure. NSTF OER electrodes [6] consist of a dispersed matrix of NSTF catalyst powder particles within a perfluorosulfonic acid (PFSA) ionomer binder (Fig. B), which have high catalyst utilization due to the high electronic conductivity of the primary catalyst particles.One of the key challenges associated with development of OER catalysts and electrodes is the lack of qualified accelerated stress tests (ASTs) to enable rapid assessments of durability under conditions relevant for end-use. The challenge is in part magnified by the long lifetime requirements of 80,000 hours and low required decay rates of single microvolts per hour, which traditionally has required long testing times and multiple replicates to obtain needed statistical significance. Additionally, evaluations of durability have often occurred under steady state testing with fixed current densities, which do not reflect anticipated use profiles when integrated with renewables such as wind and solar with significant power production variability over time. Lastly, operation at increased stack power densities is considered a key strategy to reduce stack capital costs and Ir requirements on a gram per kW basis.In this paper, we will report recent work on our durability assessment of NSTF OER powder catalysts and electrodes under aggressive testing protocols with low catalyst loadings relevant for PEM electrolyzers at large scale. Assessments included steady state durability tests, an accelerated stress test, and a protocol intended to simulate integration with a wind variable renewable energy (VRE) load profile. An example of results from the wind VRE protocol are summarized in Figs. C and D. The wind VRE protocol generated by Alia et al. [7] was modified from voltage control to current control and the maximum current density was scaled to 4.5A/cm2. The protocol was applied to a 3M laboratory CCM comprising a 0.20 mg/cm2 of 78wt% Ir/NSTF powder catalyst OER electrode, 0.09 mg/cm2 of 78wt% Pt/NSTF powder hydrogen evolution reaction (HER) electrode, and a 100 micron thick PEM (800EW 3M PFSA). After 500 hours of the wind VRE protocol, the cell performance was essentially unchanged (1mV voltage decrease at 2A/cm2). S. Dept. of Energy “H2@Scale”, https://www.energy.gov/eere/fuelcells/h2scale.S. Dept. of Energy H2USA Model, Current Forecourt Hydrogen Production v. 3.101.Ayers et al., Catalysis Today 262 121-132 (2016).Babic et al., Electrochem. Soc. 164 F387 (2017).Debe et al., ECS Trans. 45(2) 47-68 (2012).Steinbach et al., 2019 U.S. DOE Annual Merit Review, Project ta026.Alia et al., Electrochem. Soc. 166 F1164 (2019). Figure 1

Wind Put Barrier Options Pricing Based on the Nordix Index

Wind power generators face risks derived from fluctuations in market prices and variability in power production, generated by their high dependence on wind speed. These risks could be hedged using weather financial instruments. In this research, we design and price an up-and-in European wind put barrier option using Monte Carlo simulation. Under the existence of a structured weather market, wind producers may purchase an up-and-in European wind barrier put option to hedge wind fluctuations, allowing them to recover their investments and maximise their profits. We use a wind speed index as the underlying index of the barrier option, which captures risk from wind power generation and the Autoregressive Fractionally Integrated Moving Average (ARFIMA) to model the wind speed. This methodology is applied in the Colombian context, an electricity market affected by the El Niño phenomenon. We find that when the El Niño phenomenon occurs, there are incentives for wind generators to sell their energy to the system because their costs, including the put option price, are lower than the power prices. This research aims at encouraging policymakers and governments to promote renewable energy sources and a financial market to trade options to reduce uncertainty in the electrical system due to climate phenomena.

Parallel PV Configuration with Magnetic-Free Switched Capacitor Module-Level Converters for Partial Shading Conditions

In this paper, a module-level photovoltaic (PV) architecture in parallel configuration is introduced for maximum power extraction, under partial shading (PS) conditions. For the first time, a non-regulated switched capacitor (SC) nX converter is a used at the PV-side conversion stage, whose purpose is just to multiply the PV voltage by a fixed ratio and accordingly reduce the input current. All the control functions, including the maximum power point tracking, are transferred to the grid-side inverter. The voltage-multiplied PV modules (VMPVs) are connected in parallel to a common DC-bus, which offers expandability to the system and eliminates the PS issues of a typical string architecture. The advantage of the proposed approach is that the PV-side converter is relieved of bulky capacitors, filters, controllers and voltage/current sensors, allowing for a more compact and efficient conversion stage, compared to conventional per-module systems, such as microinverters. The proposed configuration was initially simulated in a 5 kW residential PV system and compared against conventional PV arrangements. For the experimental validation, a 10X Gallium Nitride (GaN) converter prototype was developed with a flat conversion efficiency of 96.3% throughout the power range. This is particularly advantageous, given the power production variability of PV generators. Subsequently, the VMPV architecture was tested on a two-module 500 WP prototype, exhibiting an excellent power extraction efficiency of over 99.7% under PS conditions and minimal DC-bus voltage variation of 3%, leading to a higher total system efficiency compared to most state-of-the-art configurations.

The effects of Open Cellular Convection on Wind Farm Operation and Wakes

Majority of the severe variability in power production of an offshore wind farm occurs when open cellular convection (OCC) is observed. With a diameter of 10-80 km, the open cells are essentially the main drivers of hour-scale wind fluctuations passing through the wind farm. Here we aim to quantify the impact of the OCC on Horns Rev-I offshore wind farm located in the North Sea, in terms of variance in the power production and turbulence intensity. Using mesoscale simulations, met-mast measurements and high frequency (1 Hz) SCADA data from all the operating turbines, the behaviour of power deficit and added turbulence intensity is explored comparatively with and without presence of open cells. The investigation is a case study performed on a ‘day-to-day’ basis with an in depth analysis of the in-farm effects, such as the wake behaviour and smaller scale atmospheric structures. For the investigated event, the study shows striking difference in wind farm operation under the open cell structures and underlines the importance of taking local mesoscale phenomena into account for wind farm operation monitoring and control, short-term wake estimation, forecasting and market participation.

Impact of weather regimes on wind power variability in western Europe

We have assessed the dependency of wind power resources in Western Europe on the atmospheric circulation as represented by a new set of 8 tailored weather regimes (WRs). For this purpose, we have derived wind capacity factors (CFs) from a meteorological reanalysis dataset and from high-resolution data simulated by the Weather Research and Forecasting (WRF) model. We first show that WRs capture effectively year-round onshore wind power production variability across Europe, especially over northwestern/central Europe and Iberia. Since the influence of the large-scale circulation on wind energy production is regionally dependent, we have then examined the high-resolution CF data interpolated to the location of more than 100 wind farms in two regions with different orography and climatological features, the United Kingdom and the Iberian Peninsula. The use of the monthly frequencies of occurrence of WRs as predictors in a multi-linear regression model allows explaining up to two thirds of the month-to-month CF variability for most seasons and sub-regions. These results outperform those previously reported based on Euro-Atlantic modes of atmospheric circulation, indicating that the use of WRs customized to the region of study is preferred to reproduce the evolution of wind energy resources. Finally, we have applied these WRs to understand the day-to-day evolution of specific episodes with anomalous regional wind power production. In particular, the wind energy deficit of summer 2018 in the United Kingdom and the surplus of March 2018 in Iberia stemmed from the combination of WRs associated with low and high CFs, respectively. These findings are relevant for the forecast of wind energy resources as the large-scale features of the atmospheric circulation captured by WRs can be modelled with considerably less uncertainty than wind speeds at wind farm sites.

Assessing the stability of wind resource and operating conditions

Wind energy is both a key potential mechanism to reduce climate forcing and a ‘weather-dependent’ energy source. Thus, while wind energy is making an increasing contribution to mitigation of human-induced climate change, climate variability and change have the potential to induce changes in both the average (expected) wind resource, the inter-annual variability in power production and the conditions in which wind turbines will operate. We present simulations with the Weather Research and Forecasting (WRF) model conducted at 12 km grid-spacing (resolution) over the eastern USA and use them to quantify the spatiotemporal variability in one aspect of wind turbine operating conditions (extreme wind speeds) and possible changes in the magnitude and interannual variability of expected wind power generation. We also discuss possible approaches that can be applied to assess the differential credibility of model-derived assessment of these properties at different locations using examples drawn from the eastern US.

Robust Transmission Expansion Planning Representing Long- and Short-Term Uncertainty

This paper addresses the transmission expansion planning problem under long- and short-term uncertainty. Long-term uncertainty pertains to changes across years (demand growth and future increase in production capacity), whereas short-term uncertainty pertains to changes within a year (demand and wind/solar power production variability, and equipment availability). This expansion problem is formulated as an adaptive robust optimization problem that provides protection against long-term uncertainty while carefully representing short-term uncertainty via scenarios. The problem is solved via a tailored implementation of the primal Benders’ decomposition algorithm that focuses on an efficient solution of the subproblem. The effectiveness of the proposed algorithm to identify robust expansion plans and its computational efficiency are illustrated through a realistic case study.

Extreme prices in electricity balancing markets from an approach of statistical physics

An increase in energy production from renewable energy sources is viewed as a crucial achievement in most industrialized countries. The higher variability of power production via renewables leads to a rise in ancillary service costs over the power system, in particular costs within the electricity balancing markets, mainly due to an increased number of extreme price spikes. This study analyzes the impact of an increased share of renewable energy sources on the behavior of price and volumes of the Italian balancing market. Starting from configurations of load and power production, which guarantee a stable performance, we implement fluctuations in the load and in renewables; in particular we artificially increase the contribution of renewables as compared to conventional power sources to cover the total load. We then determine the amount of requested energy in the balancing market and its fluctuations, which are induced by production and consumption. Within an approach of agent-based modeling we estimate the resulting energy prices and costs. While their average values turn out to be only slightly affected by an increased contribution from renewables, the probability for extreme price events is shown to increase along with undesired peaks in the costs. Our methodology provides a tool for estimating outliers in prices obtained in the energy balancing market, once data of consumption, production and their typical fluctuations are provided.

Operational and financial performance of fossil fuel power plants within a high renewable energy mix

AbstractThe operation of conventional power plants in the 2030 high-renewable energy system of central Europe with high penetration of renewables is simulated in this work. Novel insights are gained in this work, since the generation, transmission and demand models have high geographic resolution, down to scale of individual units, with hourly temporal resolution. It is shown that the increases in the partload efficiency that optimize gas power plants’ financial performance in 2030 are highly dependent on the variability in power production of renewable power plants that are in close proximity to the gas power plants. While coal power plants are also cycled more, an increased baseload efficiency is more beneficial for their financial viability. Thus, there is a need for OEMs to offer a wide range of technology solutions to cover all customers’ needs in electricity markets with high penetrations of renewables. Therefore there is an increased investment risk for OEMs as they strive to match their customers’ future needs.

The impact of new cathode materials relative to baseline performance of microbial fuel cells all with the same architecture and solution chemistry

A review of the literature using cube-type microbial fuel cell reveals the extent in variability of power production.

Design of a Cleaning Program for a PV Plant Based on Analysis of Energy Losses

Solar photovoltaic (PV) energy has grown significantly over the past few years. However, despite the increase in installed capacity, this energy source still raises important concerns related to the variability of power production. The short-term effects such as cloud shadowing and supply interruptions, as well as long-term effects such as dust accumulation, seasonal variation, and ageing of PV modules, can cause variability of power production. Therefore, the analysis of all the variability sources in order to provide statistically consistent power production data is an important challenge. This study presents a methodology to analyze data from a PV plant in order to have an independent evaluation of the different effects of variability, identifying dust deposition, and proposing a cost-based optimal cleaning program for a PV plant installed in the northern region of Chile.

Export of Norwegian Pumped Storage

The increased use of renewable energy sources in Continental Europe, in particular Germany, has led to a great variability in power production and prices. One proposed way to remedy the Continent's power balance – which is also economically viable in a free power market – is by the means of Norwegian pumped storage. However, the profitability in such an environment is highly dependent upon the extent of price variation in the market.In this paper, it has therefore been sought to find a stochastic price model in which the spot price is allowed to fluctuate around a sound forecast. With historical data from the German power market as the point of departure, a deterministic price curve with seasonal and daily patterns has been obtained through linear regression. This curve has been adjusted to the market expectations contained in power futures contracts. By contrasting the updated deterministic price curve with the actual spot price, it has been possible to obtain a time series model on the basis of deviations that are mainly stochastic.The time series model has been used to generate multiple spot price scenarios that serve as an input for a representative Norwegian pumped storage power station. Simulations show that both the power station's production planning and revenues are dependent on which scenario that is under consideration. Nonetheless, the production patterns under both the different scenarios and the real spot price are comparable, in particular with regards to the daily and weekly patterns. Similarly, the total profits depend on the variance of the price scenarios, but all scenarios, including the actual spot price, has been shown to yield significant revenues.

Influence of Environmental Temperature on 40 km Cycling Time-Trial Performance

The purpose of this study was to examine the effect of environmental temperature on variability in power output, self-selected pacing strategies, and performance during a prolonged cycling time trial. Nine trained male cyclists randomly completed four 40 km cycling time trials in an environmental chamber at 17°C, 22°C, 27°C, and 32°C (40% RH). During the time trials, heart rate, core body temperature, and power output were recorded. The variability in power output was assessed with the use of exposure variation analysis. Mean 40 km power output was significantly lower during 32°C (309 ± 35 W) compared with 17°C (329 ± 31 W), 22°C (324 ± 34 W), and 27°C (322 ± 32 W). In addition, greater variability in power production was observed at 32°C compared with 17°C, as evidenced by a lower (P = .03) standard deviation of the exposure variation matrix (2.9 ± 0.5 vs 3.5 ± 0.4 units, respectively). Core temperature was greater (P < .05) at 32°C compared with 17°C and 22°C from 30 to 40 km, and the rate of rise in core temperature throughout the 40 km time trial was greater (P < .05) at 32°C (0.06 ± 0.04°C·km-1) compared with 17°C (0.05 ± 0.05°C·km-1). This study showed that time-trial performance is reduced under hot environmental conditions, and is associated with a shift in the composition of power output. These finding provide insight into the control of pacing strategies during exercise in the heat.

Simulation-based evaluation of tidal current power output characteristics

Tidal current is considered to be one of the promising alternative green energy resources. This paper introduces a simulation based methodology to evaluate tidal current power output characteristics, including capacity factor, and power production variability patterns. Power variability analysis is crucial to grid integration systems design. The proposed methodology is applied to the well known Minas Channel in the Bay of Fundy (Atlantic Canada).