The problem of optimally trading in the continuous intraday electricity for a VPP with thermal assets is addressed by modeling a bilevel multistage stochastic programming model. The upper level of the model accounts for the profit maximization of the VPP with explicit modeling of the technical constraints of the operational status of the thermal power plant. The technical constraints of the thermal power plant for self-scheduling include minimum start-up, shut-down times, ramp-up and ramp-down rates and minimum generation level. Due to the design of the continuous intraday market, it is possible to submit buy and sell orders simultaneously for multiple delivery products. These orders are computed in the upper-level model by taking into account the technical limits of the thermal unit. The lower level model accounts for the continuous market clearing accounting for the minimum generation level of the thermal power plant. The binary variables introduced in the lower-level model give rise to the need for computing the convex hull of the lower-level model to be able to leverage KKT conditions to convert the bilevel model into a single-level equivalent model. Stochastic Dual Dynamic Integer Programming algorithm is leveraged to tackle the multistage stochastic programming problem.  

The increasing importance of cross-border trade has brought the topic of allocating cross-border transmission capacity under the limelight. In this paper, we focus on two different transmission capacity calculation methods, namely, Available Transfer Capacity and Flow-based Capacity allocation, in the continuous intraday electricity market. The effect of these two methods is studied on the continuous intraday electricity market through an agent-based model. The model comprises market participants, including renewable producers, consumers, storage, and thermal power producers. The market operator agent is responsible for managing the shared order book and clearing orders while the transmission system operator agent calculates and updates the cross-border transmission capacities in the continuous intraday electricity market. The price-volume decisions for the orders posted by the market participants are determined by two different strategies where one of them adapts to changing market conditions and new information while the other is naive. The model accounts for ramping constraints which enables the analysis of the trading behavior and interaction of agents across multiple delivery products simultaneously. A switch parameter is availed in the trading timeline for the thermal and storage agents to change from a less conservative approach of ignoring the ramping and charging/discharging rate constraints respectively to considering them. An interplay between different switch parameters and cross-border transmission capacity calculation methods is studied.

The stochastic nature of renewable energy sources has increased the need for intraday trading in electricity markets. Intradaymarkets provide the possibility to the market participants to modify their market positions based on their updated forecasts. In this paper, we propose a multistage stochastic programming approach to model the trading of a Virtual Power Plant (VPP), comprising thermal, wind and hydro power plants, in the Continuous Intraday (CID) electricity market. The order clearing in the CID market is enabled by the two presented models, namely the Immediate Order Clearing (IOC) and the Partial Order Clearing (POC). We tackle the proposed problem with a modified version of Stochastic Dual Dynamic Programming (SDDP) algorithm. The functionality of our model is demonstrated by performing illustrative and large scale case studies and comparing the performance with a benchmark model.

The growing prominence of electric vehicle (EV) aggregators in the modern power system is drawing more attention towards modeling their behavior in the short-term electricity markets. The demand-side flexibility offered by the EVs can be leveraged to reduce their charging costs. In this paper, the participation of an EV aggregator in the intraday and balancing market is modeled as a multistage stochastic programming problem. The computational complexity introduced by the peculiarities of the intraday market is solved by a progressive hedging algorithm (PHA), a scenario-based decomposition technique. A randomized scenario sampling approach is implemented to accelerate the PHA which is further improved with a parallel randomized PHA. Finally, an asynchronous version of the parallel randomized PHA is leveraged to speed up the multistage model of EV aggregator trading. We compare the computation time of the modified versions of the PHA algorithm with the conventional PHA for the proposed EV aggregator model. Furthermore, we also show the value of EV aggregator trading in the intraday and balancing markets by comparing its cost to baseline models.

The intraday electricity market provides the possibility to trade closer to real-time, aiming at assisting the market participants in improving their market position, based on their updated forecasts. The growing popularity of the intraday markets coupled with the single intraday coupling (SIDC) project in Europe drives certain trends and trading behaviours, that are analysed in this paper. Further, we present different approaches to transform the price data of the historical trades into price time series. The price time series obtained is then leveraged to derive insights on the correlation of the prices in the day-ahead and intraday market; wind power and hydropower generation, and demand time series.

In this paper, three single-stage stochastic programs are proposed and compared for optimal dispatch by a System Operator (SO) into balancing markets (BM).The motivation for the models is to represent a possible requirement to undertake system balancing with increasing amounts of intermittent renewable generation. The proposed optimization models are reformulated as tractable Mixed Integer Linear Programs (MILPs) and these consider both fuel cost and intermittency cost of the generators, when the SO activates the up- or down-regulation bids. These three models are based on the main approaches seen in practice: dual-imbalance pricing, single imbalance pricing and single imbalance pricing with spot reversion. A scenario-generation algorithm based on predictive conditional dynamic density distributions is also proposed. We perform a comparative analysis of these three proposed models in terms of how they help the SO to optimize their balancing market actions considering intermittent-renewable generators. The single imbalance pricing is found to be the most market efficient.

The surge in intermittent renewable energy sources has led to larger volumes traded in the short-term electricity markets. As a result, the problem of optimally trading in the cross-border continuous intraday (CID) electricity market has come under the limelight. In this paper, we propose a multistage stochastic programming model to determine the participation of a virtual power plant (VPP), comprising wind power, hydropower, and thermal power portfolio, in a cross-border CID market. The VPP determines the order volumes to be submitted at the market price by assessing the available orders in other bidding areas. We also present an order clearing model that accounts for the cross-border transmission capacities along with the liquidity in the CID market. The risk-averse behavior of the VPP is captured by the nested conditional value at risk (CVaR) formulation. In order to demonstrate the functionality of the proposed model, several case studies are performed by constructing data models based on historical trading data obtained from the Nord Pool.

The increasing variable renewable energy sourcesin the power system have led to rise in the trading volumesin the electricity markets. In this paper, we extend an existingopen source agent-based model for simulating the behavior andinteractions of the renewable, consumer, thermal, and marketoperator agents in the continuous intraday (CID) electricitymarket by introducing the energy storage agents. Furthermore,the limitation of the earlier model to account for the CID tradeof a single delivery product is relaxed by extending the modelto consider the simultaneous trading of all the possible deliveryproducts in a day. A realistic trading behavior is enabled byan user-defined parameter for the thermal and storage agentsto choose the switching point in the trading timeline when thestrategy of the trading agent navigates from increasing theirprofits by trading in the CID market to avoiding any imbalancesby considering their physical constraints. Comparative casestudies are performed to demonstrate the evolution of the tradingbehavior of the agents throughout the trading horizons formultiple delivery products with different switching instances.

In recent years, the vast penetration of renewable energy sources has introduced a large degree of uncertainty into the power system, thus leading to increased trading activity in the continuous intra-day electricity market. In this paper, we propose an agent-based modeling framework to analyze the behavior and the interactions between renewable energy sources, consumers and thermal power plants in the European Continuous Intra-day (CID) market. Additionally, we propose a novel adaptive trading strategy that can be used by the agents that participate in CID market. The agents learn how to adapt their behavior according to the arrival of new information and how to react to changing market conditions by updating their willingness to trade. A comparative analysis was performed to study the behavior of agents when they adopt the proposed strategy as opposed to other benchmark strategies. The effects of unexpected outages and information asymmetry on the market evolution and the market liquidity were also investigated.