Industrial Demand Side Management Formulation for Simultaneous Electricity Load Commitment and Future Load Prediction

While the majority of electricity in the Nordic and Baltic countries is traded at the day-ahead market, producers and consumers can use the intraday market to adjust their production commitments according to updated forecasts closer to the time of delivery. This reduces the need for balancing, particularly important for VRE producers, but it also means that the price formation at intraday market can change optimal bidding strategy in the day-ahead market. Through econometric modelling of intraday price premiums, this paper investigates intraday price asymmetries, which potentially can lead to strategic bidding. The intraday market is per definition symmetric, as prices for power sales always correspond to prices for power purchases, however, we find that this symmetry is not reflected in the price structure in regards to the total load adjustment needs.

The Impact of Intraday Markets on the Market Value of FlexibilityyDecomposing Effects on Profile and the Imbalance Costs

Forecast value considering energy pricing in California

Multi-market demand response using economic model predictive control of space heating in residential buildings

In day-ahead markets, participants submit bids specifying the amounts of energy they wish to buy or sell and the price they are prepared to pay or receive. However, the dynamic for forming the Market Clearing Price (MCP) dictated by the bidding mechanism is frequently overlooked in the literature on energy market modeling. Forecasting models usually focus on predicting the MCP rather than trying to build the optimal supply and demand curves for a given price scenario. This article develops a data-driven approach for generating optimal offering curves using Deep Deterministic Policy Gradient (DDPG), a reinforcement learning algorithm capable of handling continuous action spaces. Our model processes historical Italian electricity price data to generate stepwise offering curves that maximize profit over time. Numerical experiments demonstrate the effectiveness of our approach, with the agent achieving up to 85% of the normalized reward, i.e. the ratio between actual profit and the maximum possible revenue obtainable if all production capacity were sold at the highest feasible price. These results demonstrate that reinforcement learning can effectively capture complex temporal patterns in electricity price data without requiring explicit forecast models, providing market participants with adaptive bidding strategies that improve profit margins while accounting for production constraints. • Reinforcement Learning algorithm for optimizing bidding strategies of an electricity supplier. • Supplier submits offering curves based on historical electricity prices and production cost refining strategy through reward feedback. • Deep Deterministic Policy Gradient algorithm handles high-dimensional continuous action space, calibration with real-world data.

Unveiling the nexus between energy storage and electricity markets in academic publications. A data-driven analysis of emerging trends and market dynamics using NLP, sentiment analysis and probabilistic modeling

A Generation Company (GenCo) can participate in the Iberian Electricity Market (MIBEL) through different mechanisms and pools: the bilateral contracts, the physical derivatives products at the Derivatives Market, the bids to the Day-Ahead Market, the Intraday Markets or the Ancillary Services Markets. From the short-term generation planning point of view, the most important problem to solve is the bidding strategy for the Day-Ahead Market (DAM) given that the 85% of the physical energy traded in Spain is negotiated in it, but this participation cannot be tackled independently of other subsequent markets.

This paper proposes a probabilistic optimization method that produces optimal bidding curves to be submitted by an aggregator to the day-ahead electricity market and the intraday market, considering the flexible demand of his customers (based in time dependent resources such as batteries and shiftable demand) and taking into account the possible imbalance costs as well as the uncertainty of forecasts (market prices, demand, and renewable energy sources (RES) generation). The optimization strategy aims to minimize the total cost of the traded energy over a whole day, taking into account the intertemporal constraints. The proposed formulation leads to the solution of different linear optimization problems, following the natural temporal sequence of electricity spot markets. Intertemporal constraints regarding time dependent resources are fulfilled through a scheduling process performed after the day-ahead market clearing. Each of the different problems is of moderate dimension and requires short computation times. The benefits of the proposed strategy are assessed comparing the payments done by an aggregator over a sample period of one year following different deterministic and probabilistic strategies. Results show that probabilistic strategy reports better benefits for aggregators participating in power markets.

The liberalization of the Italian electricity market is a decade old. Within these last ten years, the supply side has been extensively analyzed, but not the demand side. The aim of this paper is to provide a new method for estimation of the demand elasticity, based on Bayesian methods applied to the Italian electricity market. We used individual demand bids data in the day-ahead market in the Italian Power Exchange (IPEX), for 2011, in order to construct an aggregate demand function at the hourly level. We took into account the existence of both elastic and inelastic bidders on the demand side. The empirical results show that elasticity varies significantly during the day and across periods of the year. In addition, the elasticity hourly distribution is clearly skewed and more so in the daily hours. The Bayesian method is a useful tool for policy-making, insofar as the regulator can start with a priori historical information on market behavior and estimate actual market outcomes in response to new policy actions.

Purpose Propose an approximation procedure to efficiently represent aggregate supply and demand curves in the electricity market. Design/methodology/approach Two approximation procedures based on one-step functions are designed using the L1 and L2 metrics as error measures. For the metric L2 a closed-form solution is obtained to reduce the global error over the entire domain. For the metric L1, which lacks a closed-form solution, a linear programming problem is solved to improve the local approximation within intervals defined by the nodes. The dependence on the node locations is addressed by different node selection strategies. In particular, a heuristic strategy is proposed that combines descriptive information from the offers with a dyadic search procedure to minimize the approximation error. Findings The performance of our proposals is evaluated and compared using curves from the day-ahead Spanish electricity market. Our procedure achieves a promising approximation performance compared with existing approaches. Practical implications The proposed procedures will allow the development of efficient methods for forecasting supply and demand curves and, consequently, market clearing prices. Having these forecasts is of interest to both producers and consumers. Originality/value Existing procedures for representing step supply and demand curves often involve high computational costs or, alternatively, make assumptions of smoothness and differentiability of the curves that contradict the nature of these step curves. Our proposals address these challenges, obtaining approximations that resemble real curves in a parsimonious and practical way.

In a day-ahead electricity market, competitive bidding strategy plays a vital role for power suppliers to maximize their profit. In this type of market, each power supplier submits a set of hourly production prices and offers capacity for the next period. The market operator, after receiving this data along with forecasted hourly load from the demand side, allocates production output to each unit. Power suppliers face the problem in trading their offers in the market, due to the uncertain behavior of competitive power suppliers and power demand. Therefore, the power supplier requires a suitable bidding strategy for handling uncertainty in the market to maximize their profits. Moreover, the considerations of ramp rates are necessary for the precise representation of practical power system. Thus, in the present work, a modified gravitational search algorithm based on oppositional learning concept is used to solve strategic bidding problem for power suppliers considering six generators with ramp rates, 24-h load data and rivals behavior. The total hourly profits of generators with and without considering ramp rates have been compared.

The fundamental modeling of energy systems through individual unit commitment decisions is crucial for energy system planning. However, current large-scale models are not capable of including uncertainties or even risk-averse behavior arising from forecasting errors of variable renewable energies. However, risks associated with uncertain forecasting errors have become increasingly relevant within the process of decarbonization. The intraday market serves to compensate for these forecasting errors. Thus, the uncertainty of forecasting errors results in uncertain intraday prices and quantities. Therefore, this paper proposes a two-stage risk-constrained stochastic optimization approach to fundamentally model unit commitment decisions facing an uncertain intraday market. By the nesting of Lagrangian relaxation and an extended Benders decomposition, this model can be applied to large-scale, e.g., pan-European, power systems. The approach is applied to scenarios for 2023—considering a full nuclear phase-out in Germany—and 2035—considering a full coal phase-out in Germany. First, the influence of the risk factors is evaluated. Furthermore, an evaluation of the market prices shows an increase in price levels as well as an increasing day-ahead-intraday spread in 2023 and in 2035. Finally, it is shown that intraday cross-border trading has a significant influence on trading volumes and prices and ensures a more efficient allocation of resources.

In advanced power systems, to balance load generation mismatches in the face of unexpected events and to meet unforeseen possibilities, load taking and shedding instructions are required under Ancillary Services (AS), which are performed by providing spinning reserve (SR) in the units. SR is an important factor, which is rather uncertain to predict due to the unpredictability of customers’ consumption, excessive or under-energy generation, and unpredictability in the integration of renewable energy sources. In this study, integration of renewable energy is addressed as a factor for predicting SR. Increasing the integration of solar and wind into the power system requires larger amounts of SR, which creates a significant increase in generation and emission costs. Optimum estimation of SR capacity helps system operators (SO) plan generators in advance and in a better bidding environment. In the past, estimation tools such as feed forward networks and time series models have been used to estimate load and electricity generation. In this article, Extreme Learning Machine (ELM) method is used to estimate SR capacity in the day-ahead and intraday market. The results obtained with ELM were also compared with the results observed with ANN, LR and SVM methods.

The aim of this chapter is to present an improved version of incentive-based demand response program (IBDRP). Firstly, the formulation of the basic exponential IBDRP is explained to manifest the merits of the presented model. Then, the mathematical formulation of the improved IBDRP is explained which has three main novelties. The first merit is that the proposed model has improved the elasticity concept by considering it as a function of consumer type and consumption hour. In the second novelty, the incentive payments are not constant and are considered as a function of peak intensity. During the third one, the effects of intra-day electricity market are considered in formulation, too. This feature makes consumers able to provide more realistic participation in IBDRP. Finally, the authors have computed peak shaving and economic indices of two case studies in order to evaluate the efficiency of the proposed demand response model.

Maximum power demand management is an embedded based project which may be enforced in industries wherever quantity of active load on the idea of critical and non-critical have to be monitored and controlled. Industries are required to use a private controller to dominate these individual parameters. The aim of this project is to use one controller for distributing power from multiple feeders in sequence, just in case of load overflow and use of generator during an absolutely automatic mode on the idea of prioritizing critical and non-critical loads. This project is helpful for power management in industries, schools and large consumers. In many countries, there is an inadequacy of power. Due to this, the electricity board provides solely restricted quantity of energy to the industries, instructional establishments and different large consumers. If they exceed the restricted quantity, they need to pay penalty. The main objective of our project is to share the total dynamic line load for each phase as per the pre defined feeder rating. When the value is exceeded for specific single feeder, the phase will automatically share the load to the other feeders whenever the demand is required. And when the maximum power is used, i.e., overload occurs, the system will automatically start a generator and the parameters will be monitored and controlled by using embedded system. Keywords: Maximum power demand, critical and non critical loads, dynamic line load, embedded system Cite this Article Md. Humayun Kabir, Md. Iktiham Bin Taher, Shams Ul Islam et al. A Pragmatic Approach for Managing Maximum Power Demand with Generator Control Mechanism Using PIC Microcontroller. Journal of Power Electronics & Power Systems . 2018; 8(2): 11–18p.