Demand Response In Power Systems Research Articles

Secure Distributed Control for Demand Response in Power Systems Against Deception Cyber-Attacks With Arbitrary Patterns

Secure Distributed Control for Demand Response in Power Systems Against Deception Cyber-Attacks With Arbitrary Patterns

Classifying and modelling demand response in power systems

Demand response (DR) is expected to play a major role in integrating large shares of variable renewable energy (VRE) sources in power systems. For example, DR can increase or decrease consumption depending on the VRE availability, and use generating and network assets more efficiently. Detailed DR models are usually very complex, hence, unsuitable for large-scale energy models, where simplicity and linearity are key elements to keep a reasonable computational performance. In contrast, aggregated DR models are usually too simplistic and therefore conclusions derived from them may be misleading. This paper focuses on classifying and modelling DR in large-scale models. The first part of the paper classifies different DR services, and provides an overview of benefits and challenges. The second part presents mathematical formulations for different types of DR ranging from curtailment and ideal shifting, to shifting including saturation and immediate load recovery. Here, we suggest a collection of linear constraints that are appropriate for large-scale power systems and integrated energy system models, but sufficiently sophisticated to capture the key effects of DR in the energy system. We also propose a mixed-integer programming formulation for load shifting that guarantees immediate load recovery, and its linear relaxation better approximates the exact solution compared with previous models.

Residential load forecasting based on LSTM fusing self-attention mechanism with pooling

Day-ahead residential load forecasting is crucial for electricity dispatch and demand response in power systems. Electrical loads are characterized by volatility and uncertainty caused by external factors, especially for individual residential loads. With the deployment of advanced metering infrastructure, the acquisition of electricity consumptions of multiple residential customers is available. This paper proposes a novel day-ahead residential load forecasting method based on feature engineering, pooling, and a hybrid deep learning model. Feature engineering is performed using two-stage preprocessing on data from each user, i.e., decomposition and multi-source input dimension reconstruction. Pooling is then adopted to merge data from both the target user and its interconnected users, in a descending order based on mutual information. Finally, a hybrid model with two input channels is developed by combining long short-term memory (LSTM) with self-attention mechanism (SAM). The case studies are conducted on a practical dataset containing multiple residential users. Performance of the proposed load forecasting method using data pools of different groups of users as well as different input forms is compared. The effectiveness of input dimension reconstruction and hybrid model is also validated. The overall results demonstrate the superiority of the proposed load forecasting method through comparison with other benchmark methods.

Modelling the Integration of Residential Heat Demand and Demand Response in Power Systems with High Shares of Renewables

The EU aims to become the world’s first climate-neutral continent by 2050. In order to meet this target, the integration of high shares of Renewable Energy Sources (RESs) in the energy system is of primary importance. Nevertheless, the large deployment of variable renewable sources such as wind and photovoltaic power will pose important challenges in terms of power management. For this reason, increasing the system flexibility will be crucial to ensure the security of supply in future power systems. This work investigates the flexibility potential obtainable from the diffusion of Demand Response (DR) programmes applied to residential heating for different renewables penetration and power system configuration scenarios. To that end, a bottom-up model for residential heat demand and flexible electric heating systems (heat pumps and electric water heaters) is developed and directly integrated into Dispa-SET, an existing unit commitment optimal dispatch model of the power system. The integrated model is calibrated for the case of Belgium and different simulations are performed varying the penetration and type of residential heating technologies, installed renewables capacity and capacity mix. Results show that, at country level, operational cost could be reduced up to €35 million and curtailment up to 1 TWh per year with 1 million flexible electric heating systems installed. These benefits are significantly reduced when nuclear power plants (non-flexible) are replaced by gas-fired units (flexible) and grow when more renewable capacity is added. Moreover, when the number of flexible heating systems increases, a saturation effect of the flexibility is observed.

Predictive online convex optimization

We incorporate future information in the form of the estimated value of future gradients in online convex optimization. This is motivated by demand response in power systems, where forecasts about the current round, e.g., the weather or the loads’ behavior, can be used to improve on predictions made with only past observations. Specifically, we introduce an additional predictive step that follows the standard online convex optimization step when certain conditions on the estimated gradient and descent direction are met. We show that under these conditions and without any assumptions on the predictability of the environment, the predictive update strictly improves on the performance of the standard update. We give two types of predictive update for various family of loss functions. We provide a regret bound for each of our predictive online convex optimization algorithms. Finally, we apply our framework to an example based on demand response which demonstrates its superior performance to a standard online convex optimization algorithm.

Fostering Residential Demand Response through Developing Proactive and Elastic Demand Approaches. An Overview DR

Demand response (DR) is one of the major stakeholders in the smart grid and has been used as an energy reconciler between supply and demand. After a literature overview, the importance of the paper is enhanced by having a theoretical and behavioral-based analysis of DR in power systems. In this work, the potential factors that influence more DR among customers and the residential market as a whole have been discussed. The customers’ elastic demand approach can pave the way for adapting a responsive demand mechanism that ensures the system reliability and cost effective measures. Alternatively, this approach can make the program more effective and supportive in serving the social welfare as whole.

The Multi-Armed Bandit With Stochastic Plays

We extend the stochastic multi-armed bandit to the case where the number of arms to play evolves as a stationary process. Our work is motivated by demand response in power systems, in which the number of arms to play, or loads to dispatch, depends on a random power imbalance. We give an upper confidence bound-based algorithm that achieves sublinear pseudo-regret. We apply our results in several examples from demand response.

Implementation and Assessment of a Decentralized Load Frequency Control: Application to Power Systems with High Wind Energy Penetration

This paper describes and assesses a decentralized solution based on a wireless sensor-actuator network to provide primary frequency control from demand response in power systems with high wind energy penetration and, subsequently, with relevant frequency excursions. The proposed system is able to modify the electrical power demand of a variety of thermostatically-controlled loads, maintaining minimum comfort levels and minimizing both infrastructure requirements and primary reserves from the supply side. This low-cost hardware solution avoids any additional wiring, extending the wireless sensor-actuator network technology towards small customers, which account for over a 30% share of the current power demand. Frequency excursions are collected by each individual load controller, considering not only the magnitude of the frequency deviation, but also their evolution over time. Based on these time-frequency excursion characteristics, controllers are capable of modifying the power consumption of thermostatically-controlled loads by switching them off and on, thus contributing to primary frequency control in power systems with higher generation unit oscillations as a consequence of relevant wind power integration. Field tests have been carried out in a laboratory environment to assess the load controller performance, as well as to evaluate the electrical and thermal response of individual loads under frequency deviations. These frequency deviations are estimated from power systems with a high penetration of wind energy, which are more sensitive to frequency oscillations and where demand response can significantly contribute to mitigate these frequency excursions. The results, also included in the paper, evaluate the suitability of the proposed load controllers and their suitability to decrease frequency excursions from the demand side in a decentralized manner.

Economic evaluation of demand response in power systems with high wind power penetration

The penetration of wind power generation is expected to increase in power systems, dramatically. The unpredictable nature of the wind generation poses an obstacle to high penetration of wind energy in the electric power systems. Demand response (DR) may be considered as an efficient approach to cope with the energy unbalances caused by the wind power intermittency. Fair mechanism for pricing of the DR may increase the demand-side participation which consequently facilitates wind power integration in the power systems. This paper focuses on the economic evaluation of the DR according to its potential for mitigating the wind power forecast error in the power system operation. Demand increase, similar to the demand curtailment, is considered as a DR resource and evaluated in this paper. For this purpose, first an insight is provided into the power system operation under the high wind power penetration with the aim of extracting the DR benefits. Based on the DR benefits, a mathematical model is developed to find the maximum monetary incentive for the DR that the system operator is willing to pay to the DR providers. In the proposed model, DR's potential in reducing the cost of supplying load as well as its capability in reducing the cost of system reserve, start up and shut down of units, load shedding, and wind power spillage are considered. The results of the proposed evaluation method provide valuable information for both the system operator and demand response providers. The proposed method is implemented on an example and a realistic case study and discussions on results are presented.