Forecast value considering energy pricing in California

In this paper, the model of strategic wind power offering in the day-ahead (DA) market is proposed considering the uncertainties of wind power production, and price forecasting of DA and real-time (RT) market. The wind power deviation in the RT market is settled with the two-price mechanism based on the deviation direction and the relation between the locational marginal prices (LMPs) of DA and RT. Instead of using the point forecasting for the DA and RT LMPs, the uncertainties of LMP forecasting are modeled. In addition, the correlation between the forecasting errors of DA and RT LMP are directly modeled instead of generating the correlated scenarios. Finally, the optimal offered quantity of wind power in the DA market is derived using the probability theory based on the probabilistic wind power forecasting. The case study using the price data of actual DA and RT from Midcontinent Independent System Operator (MISO) validates the effectiveness of the proposed model. It shows that the correlation of the forecasting errors of DA and RT LMP has a significant impact on the wind power quantity offered by DA and revenue results.

In a deregulated power market, the real-time wholesale market price of electricity varies dramatically within a single day due to the availability of the resources. Moreover, the price of electricity can be different from one location to the other at the same time period due to the location of the available resources and transmission constraints. This is the so-called locational marginal price (LMP). Since wind power is noncontrollable and partially unpredictable, it is difficult to schedule its output to exploit LMP variations. While energy storage system (ESS) may accommodate wind farm output, it requires significant initial financial commitment. Accurately forecasted wind power and LMP information can reduce the required capacity and make it financially feasible for the ESS to perform desired functions. In this paper, artificial neural network (ANN) technique is employed to forecast the day-ahead wind power and LMP, and a hybrid ESS consisting of two storage facilities is developed. The primary ESS is utilized for the optimizing wind-storage system production schedule with day-ahead forecasting data, while the secondary ESS is applied to address the forecasting errors during real-time operation. With this hybrid ESS design, financial benefits are achieved for the wind farm.

In India, Short term open access (STOA) in transmission and distribution provides a range of opportunities to the market players. Sellers and buyers can transact power bilaterally through over the counter contracts or bid collectively through the power exchanges in Day Ahead Market (DAM). Real Time Market (RTM) was introduced in India from 01.06.2020. This has created a new opportunity for the electricity market players for trading in real time with a lead-time of 1.5 hours. RTM provides 48 bidding windows over a day. This provides additional flexibility for last mile portfolio optimization. This paper attempts to analyze the impact of coexistence of DAM and RTM to derive further inferences. It provides an insight to the relationship of the Market Clearing Price (MCP) of DAM and RTM. Trend of MCP of DAM and RTM is analyzed with respect to variation in demand.

This article delves into the security-constrained scheduling of a Virtual Power Plant (VPP) through a two-stage methodology. In the proposed framework, a decentralized locational marginal pricing (DLMP) based primary pricing mechanism is applied to realize the pricing in first stage. An exclusive Mixed Integer Linear Programming (MILP) framework is crafted for the VPP’s scheduling to enhance the utilization of Distributed Energy Resources (DERs) and controllable loads in both the Day Ahead Market (DAM) and Real-Time Market (RTM). The primary objective is maximizing joint profit for the VPP and the DERs, while the economic assessment hinges on Levelized Cost of Energy (LCOE) for financial evaluation of the proposed scheme. The model’s practicality is verified using the GAMS environment, and its efficiency is gauged through LCOE benchmarking. Furthermore, this paper unveils a communication framework for the VPP, grounded in IEC 61850-8-2 protocol. This framework exemplifies the exchange of XMPP stanzas among distinct clients during the strategy’s implementation.

Water vapor mixing ratios in the upper troposphere and lower stratosphere measured by the Aura Microwave Limb Sounder (MLS) version 2.2 instrument have been compared with Global Forecast System (GFS) analyses at five levels within the 300–100-hPa layer and North American Mesoscale (NAM) model analyses at six levels within the 300–50-hPa layer over the two years of 2005 and 2006 at four analysis times (e.g., 0000, 0600, 1200, and 1800 UTC). Probability density functions of the vapor mixing ratios suggest that both analyses are often moister than Aura MLS values, but NAM model analyses agree somewhat better with Aura MLS measurements than GFS model analyses over the same North American domain at the five common levels. Examining five subsets of the global GFS domain, the GFS model analysis is moister than Aura MLS estimates everywhere but at 150 and 100 hPa in all regions outside of the tropics. NAM model analysis water vapor mixing ratios exceeded the Aura MLS values at all levels from 250 to 150 hPa in all four seasons of both years and some seasons at 100 and 50 hPa. Moist biases in winter and spring of both years were similar at all levels, but these moist biases in summer and fall were smaller in 2005 than in 2006 at all levels. These differences may be due to the change in the NAM from using the Eta Model to using the Weather Research and Forecasting model (WRF) in June 2006.

As the deployment of solar power utilizing devices increases, accurate forecasts of solar energy become increasingly important for managing and stabilizing the electricity grid. This study investigates the validity of 7-day ahead global horizontal irradiance (GHI) forecasts for South Africa provided by the Global Forecast System (GFS). The clear-sky index was used to interpolate GFS mixed 3-hourly and 6-hourly averages to generate hourly forecasts. The model’s performance was validated against radiometric GHI data from 15 locations spread across South Africa’s six climatic zones. The results showed that the GFS model can satisfactorily forecast GHI in South Africa. Overall, the model performed best in interior climatic zones and relatively poorly in coastal climatic zones. The accuracy of the GFS model performance decreases as the forecast lead time increases. The model overestimates GHI in overcast conditions while it underestimates GHI in clear and partly cloudy conditions. On average, the model underestimates GHI in all seasons. Forecasting errors are highest in summer and lowest in winter and moderate in autumn and spring. The inadequacy of the GFS model to precisely determine the cloud quantities is the major source of error; errors increase as the cloud fraction increases. Despite this, the study’s findings suggest that GFS forecasts may be used for forecasting solar photovoltaic output for energy planning in South Africa. Significance Statement This paper provides the first comprehensive assessment of Global Forecast System (GFS) forecasts of global horizontal irradiance (GHI) in six climatic regions of South Africa, exploring how cloud cover, site location, climatic zone, season, forecast horizon, and hour of the day impact the model’s performance. This research was carried out in a country where GFS GHI models have never been validated before and that presents a wide range of climatic conditions. The results allow the accuracy of other global and mesoscale GHI forecasting models to be compared against the current results in the region. The outcomes of this study indicate the possibility of using GFS GHI forecasts for forecasting solar photovoltaic output for energy planning. South Africa has an electricity supply deficit, and being able to accurately forecast the output of the increasing capacity of solar PV is one of the things needed to stabilize the grid.

Robust bidding strategy for aggregation of distributed prosumers in flexiramp market

In this paper, we consider the profit-maximizing demand response of an energy customer in the real-time electricity market. In a real-time electricity market, the market clearing price is determined by the random deviation of actual power supply and demand from the predicted values in the day-ahead market. An energy customer, which requires a total amount of energy over a certain period of time, has the flexibility of shifting its energy usage in time, and therefore is in perfect position to exploit the volatile real-time market price through demand response. We show that the profit-maximizing demand response strategy can be obtained by solving a finite-horizon continuous-state Markov decision process (MDP) problem. Through rigorous analysis, we show that the optimal actual demand policy exhibits a threshold structure, which can solve the MDP without the need of discretizing the state and action spaces. We demonstrate through extensive simulations that the proposed demand response strategy not only maximizes the profit of the energy customer, but also alleviates the supply-demand imbalance in the power grid, and even reduces the bills of other market participants. On average, the proposed demand response strategy increases the energy customer's profit by 53.8% and saves the bills of other utilities by 80.4% comparing with the benchmark algorithms.

The electrical power system has the uncertainty in its operation process, from this brings the demand forecast can with actual demand existence certain deviation. The balanced from the electricity generation and the load demand may brought to be, by using of the day-ahead and real-time electricity market transaction mechanism and the demand real-time adjustment electricity generation. At the same time, to manifest the demand-side fully in the market competition function, a revised bidding strategy was studied for the demand-side under the day-ahead and real-time market, making use of two stage decision method of the newsvendor problem. The example had indicated this method had feasible.

“Comparing merit order effects of wind penetration across wholesale electricity markets”

Optimal bidding strategy of a virtual power plant in day-ahead energy and frequency regulation markets: A deep learning-based approach

Urban integrated energy system stochastic robust optimization scheduling under multiple uncertainties

Shared-constraint approach for multi-leader multi-follower game of generation companies participating in electricity markets with carbon emission trading mechanism

This study assesses forecasts of the preconvective and near-storm environments from the convection-allowing models run for the 2008 National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) spring experiment. Evaluating the performance of convection-allowing models (CAMs) is important for encouraging their appropriate use and development for both research and operations. Systematic errors in the CAM forecasts included a cold bias in mean 2-m and 850-hPa temperatures over most of the United States and smaller than observed vertical wind shear and 850-hPa moisture over the high plains. The placement of airmass boundaries was similar in forecasts from the CAMs and the operational North American Mesoscale (NAM) model that provided the initial and boundary conditions. This correspondence contributed to similar characteristics for spatial and temporal mean error patterns. However, substantial errors were found in the CAM forecasts away from airmass boundaries. The result is that the deterministic CAMs do not predict the environment as well as the NAM. It is suggested that parameterized processes used at convection-allowing grid lengths, particularly in the boundary layer, may be contributing to these errors. It is also shown that mean forecasts from an ensemble of CAMs were substantially more accurate than forecasts from deterministic CAMs. If the improvement seen in the CAM forecasts when going from a deterministic framework to an ensemble framework is comparable to improvements in mesoscale model forecasts when going from a deterministic to an ensemble framework, then an ensemble of mesoscale model forecasts could predict the environment even better than an ensemble of CAMs. Therefore, it is suggested that the combination of mesoscale (convection parameterizing) and CAM configurations is an appropriate avenue to explore for optimizing the use of limited computer resources for severe weather forecasting applications.

Large consumers’ electricity bills depend on many factors including: different electricity purchasing contracts and markets, deviation penalties, and grid fees. Two key markets are the intraday and the day-ahead markets. On the day-ahead market, the consumer commits to a load of electricity that stacks on top of their longer-term contract commitments. The next day, the consumer must follow this demand profile to avoid paying deviation penalties. The demand curve can be modified on the existing day using the intraday market. A novel demand side response formulation is proposed that considers a two-day horizon for both the intraday and the day-ahead markets by a separate modeling approach. Results show that this formulation can effectively combine intraday and day-ahead market concerns and that the resulting demand profile from the two-day problem is more realistic than current models, which consider only a single-day problem.