This paper presents a two-stage stochastic programming model for provision of flexible demand response (DR) based on thermal energy storage in the form of hot water storage and/or storage in building material. Aggregated residential electro-thermal technologies (ETT), such as electric heat pumps and (micro-) combined heat and power, are modeled in a unified, non-technology specific way. Day-ahead optimization is carried out considering uncertainty in outdoor temperature, electricity and hot water consumption, dwelling occupancy, and imbalance prices. Building flexibility is exploited through 1) specification of a deadband around the set temperature, or 2) a price of thermal discomfort applied to deviations from the set temperature. A new expected thermal discomfort (ETD) metric is defined to quantify user discomfort. The efficacy of exploiting the flexibility of various residential ETT following the two approaches is analyzed. The utilization of the ETD metric to facilitate quantification of the expected total (energy and thermal discomfort) cost is also demonstrated. Such quantification may be useful in the determination of DR contracts set up by energy service companies. Case studies for a UK residential users' aggregation exemplify the model proposed and quantify possible cost reductions that are achievable under different flexibility scenarios.

This paper proposes a security-constrained forward market clearing algorithm within which the inherent characteristics of demand flexibility are acknowledged when the provision of reserve from the demand side is considered. In the proposed formulation, we co-optimize the cost of scheduling the appropriate resources to guarantee the security of the system and the expected cost of operating under any credible system state. In addition, we consider that consumers can offer to provide spinning reserve capacity deployable by voluntary load reductions in response to contingencies. Due to the load recovery effect (i.e., since energy usage is essentially postponed when demand-side reserve is deployed), in post-contingency states, any voluntary reduction in the load has to be accompanied by a subsequent increase in demand from the initial forecast. We demonstrate that the marginal value of the reserve provided by the demand can only be calculated taking into account the cost of supplying the recovery consumption. Flexible consumers can reduce their payments on average if energy is settled through real-time prices.Index Terms-Demand-side reserve, electricity markets, energy payback, operations planning, real-time pricing, security-constrained optimal power flow, spinning reserve, stochastic optimization.

This paper reviews recent works applying machine learning techniques in the context of energy systems reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of machine learning. The objective is to foster the synergy between these two fields and speed up the practical adoption of machine learning techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, etc. can be extended to other similar systems, such as distribution systems, micro-grids, and multi-energy systems.

Abstract-This paper develops a probabilistic approach for power system reliability management in real-time operation where risk is a product of i) the potential occurrence of contingencies, ii) the possible failure of corrective (i.e., post-contingency) control and, iii) the socio-economic impact of service interruptions to end-users. Stressing the spatiotemporal variability of these factors, we argue for reliability criteria assuring a high enough probability of avoiding service interruptions of severe socio-economic impact by dynamically identifying events of nonnegligible implied risk. We formalise the corresponding decision making problem as a chance-constrained two-stage stochastic programming problem, and study its main features on the single area IEEE RTS-96 system. We also discuss how to leverage this proposal for the construction of a globally coherent reliability management framework for long-term system development, midterm asset management, and short-term operation planning.

This paper presents a two-stage stochastic programming model for provision of flexible demand response (DR) based on thermal energy storage in the form of hot water storage and/or storage in building material. Aggregated residential electro-thermal technologies (ETT), such as electric heat pumps and (micro-) combined heat and power, are modeled in a unified, non-technology specific way. Day-ahead optimization is carried out considering uncertainty in outdoor temperature, electricity and hot water consumption, dwelling occupancy, and imbalance prices. Building flexibility is exploited through 1) specification of a deadband around the set temperature, or 2) a price of thermal discomfort applied to deviations from the set temperature. A new expected thermal discomfort (ETD) metric is defined to quantify user discomfort. The efficacy of exploiting the flexibility of various residential ETT following the two approaches is analyzed. The utilization of the ETD metric to facilitate quantification of the expected total (energy and thermal discomfort) cost is also demonstrated. Such quantification may be useful in the determination of DR contracts set up by energy service companies. Case studies for a UK residential users' aggregation exemplify the model proposed and quantify possible cost reductions that are achievable under different flexibility scenarios.

Abstract-In this paper we study how supervised machine learning could be applied to build simplified models of realtime (RT) reliability management response to the realization of uncertainties. The final objective is to import these models into look-ahead operation planning under uncertainties. Our response models predict in particular the real-time reliability management costs and the resulting reliability level of the system. We tested our methodology on the IEEE-RTS96 benchmark.Among the supervised learning algorithms tested, extremely randomized trees, kernel ridge regression and neural networks appear to be the best methods for this application. Furthermore, by using feature "importances" computed by tree-based ensemble methods, we were able to extract the most relevant variables to predict the response of real-time reliability management, and thus obtain a better understanding of the system properties.

Abstract-In the context of operation planning, probabilistic reliability assessment essentially boils down to predicting, efficiently and with sufficient accuracy, various economic and reliability indicators reflecting the expected performance of the system over a certain look-ahead horizon, so as to guide the operation planner in his decision-making. In order to speedup the crude Monte Carlo approach, which would entail a very large number of heavy computations, we propose in this paper an approach combining Monte Carlo simulation, machine learning and variance reduction techniques such as control variates. We provide an extensive case study testing this approach on the three-area IEEE-RTS96 benchmark, in the context of day-ahead operation planning while using a security constrained optimal power flow model to simulate real-time operation according to the N-1 criterion. From this case study, we can conclude that the proposed approach allows to reduce the number of heavy computations by about an order of magnitude, without sacrificing accuracy.

This paper investigates the stakes of introducing probabilistic approaches for the management of power system's security. In real-time operation, the aim is to arbitrate in a rational way between preventive and corrective control, while taking into account i) the prior probabilities of contingencies, ii) the possible failure modes of corrective control actions, iii) the socio-economic consequences of service interruptions. This work is a first step towards the construction of a globally coherent decision making framework for security management from long-term system expansion, via mid-term asset management, towards shortterm operation planning and real-time operation. Nomenclature Indices:b Index of corrective control behaviors. Sets:D n Set of demands connected at node n.G n Set of generating units connected at node n. Marginal corrective re-dispatch cost of generating unit g.Capacity of generating unit g.Minimum stable generation of unit g.Ramp-down limit of generating unit g.Ramp-up limit of generating unit g. ∆P e gEmergency ramp-down limit of generating unit g.Disconnection severity coefficient of generating unit g. β n, Element of the flow incidence matrix, taking a value of one if node n is the sending node of line , a value of minus one if node n is the receiving node of line , and a zero value otherwise. M A large constant. Continuous Variables:Power output of generating unit g under the precontingency state.