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Gas-fired generators, with their ability to quickly ramp up and down their electricity production, play an important role in managing renewable energy variability. However, these changes in electricity production translate into variability in the consumption of natural gas, and propagation of uncertainty from the electric grid to the natural gas system. To ensure that both systems are operating safely, there is an increasing need for coordination and uncertainty management among the electricity and gas networks. A challenging aspect of this coordination is the consideration of natural gas dynamics, which play an important role in intra-day operation, but give rise to a set of non-linear and non-convex equations that are hard to optimize even in the deterministic case. Ideally, the problem is formulated as a stochastic problem but many conventional methods for stochastic optimization are numerically intractable because they either incorporate a large number of scenarios directly or require the underlying problem to be convex. To address these challenges, we propose using a Stochastic Hybrid Approximation algorithm to more efficiently solve these problems and investigate the efficacy of several different variants of this algorithm. Our case study demonstrates that the proposed technique is able to quickly obtain high quality solutions and outperforms existing benchmarks such as Generalized Benders Decomposition. We demonstrate that coordinated uncertainty management that accounts for the gas system can significantly reduce both electric and gas system load shed in stressed conditions. [ABSTRACT FROM AUTHOR]

Demand-side operations incentivize utility customers to take part in various grid services. A demand response enabled load (DREL) is a flexible grid asset that schedules electricity consumption in response to a time-of-use (TOU) energy price. Consequently, its energy profile differs from that of a conventional load that is insensitive to price. This difference may cause new challenges for distribution system state estimation (DSSE). It is well known that DSSE often needs to use pseudo-measurements based on historic load profiles to increase system observability. However, historic profiles of conventional loads are not representative of DREL behaviors. The inaccuracy impacts DSSE results and other DSSE-dependent operations. In this paper, we propose an online pseudo-measurement generation approach for DSSE with DRELs. We formulate an optimization model to represent DRELs self-adjusting actions. Sampling-based stochastic optimization techniques are proposed to account for uncertainties in DRELs. A set of representative DREL behavior data corresponding to the samples are used to characterize DREL pseudo-measurements. Case studies with modified IEEE 123-bus test system verify the validity of the proposed work. [ABSTRACT FROM AUTHOR]

Introducing flexible consumers to electricity markets is beneficial to the power system and offers them potential economic savings. Industrial consumers are pioneer candidates due to their high energy demand and interest in reducing energy costs. This paper addresses the battery storage and photovoltaics investment problem, which includes five revenue streams for industrial consumers, such as participation in the day-ahead and intraday energy markets as well as the primary control reserve market peak shaving and optimized self-consumption. The uncertainty is considered using correlated scenarios of the local load, primary reserve market and day-ahead market prices, as well as generation from photovoltaics. The uncertainty of the pay-as-bid intraday market with continuous trading is modeled using robust optimization. Credibility and applicability of the model is achieved by using market settings and prices from three European countries (Germany, Denmark and Croatia) and comparing their suitability for encompassing the end-user flexibility. Results shed light on national energy-political and climatic differences, highlighting opportunities for active market participation through individual or aggregated industrial plants. [ABSTRACT FROM AUTHOR]

Oil refining is an energy-intensive industry, that is often equipped with different types of distributed generation (DG) resources. Optimal energy management of the refinery's load and its DG resources, under dynamic electricity pricing, improves electricity consumption behavior of the facility and hence reduces its energy costs. An energy management system (EMS) model is proposed in this paper for minimizing electricity consumption costs of an oil refinery facility considering on-site cogeneration capability. The energy management is based on power demand modeling of the oil refinery process. A joint electrical–thermal model is used for the cogeneration units to account for the electricity and steam production. The developed EMS model is also used as part of a demand response strategy to illustrate the impact of EMS decisions on distribution system operations. [ABSTRACT FROM AUTHOR]

Multienergy systems are flexible energy systems that can benefit from energy resources to supply different energy demands. Due to the capabilities of multienergy systems in generating different energy carriers, these systems have been rapidly expanded in power systems. After restructuring in power system in recent years and appearance of competent energy markets, energy systems operated within such environments have been usually exposed to uncertainties of various parameters, such as price, demand, etc. In this paper, a novel optimization framework based on hybrid scenario-based/interval/information gap decision theory method is developed to investigate the optimal operation of smart energy hubs (S. E. Hubs) subject to economic priorities, technical constraints of the distribution network and uncertainties. Considering energy hubs equipped to smart facilities, demand-side management programs including price response and load response services have been available to motivate electrical consumers to revise their consumption pattern in order to satisfy economic priorities of energy hubs. By using the results of employed hybrid uncertainty modeling approach, the operator of S. E. Hubs can decide either to take risk-averse or risk-seeking strategy against the uncertainties. Uncertainty based integration of S. E. Hubs into distribution network is evaluated regarding the IEEE 33-bus test system and the results obtained from simulations are presented for comparison. [ABSTRACT FROM AUTHOR]

Demand-response aggregators are faced with the challenge of how to best manage numerous and heterogeneous distributed energy resources (DERs). This paper proposes a decentralized methodology for optimal coordination of DERs. The proposed approach is based on Dantzig–Wolfe decomposition and column generation, thus allowing to integrate any type of resource whose operation can be formulated within a mixed-integer linear program. We show that the proposed framework offers the same guarantees of optimality as a centralized formulation, with the added benefits of distributed computation, enhanced privacy, and higher robustness to changes in the problem data. The practical efficiency of the algorithm is demonstrated through extensive computational experiments, on a set of instances generated using data from Ontario energy markets. The proposed approach was able to solve all test instances to proven optimality, while achieving significant speed-ups over a centralized formulation solved by state-of-the-art optimization software. [ABSTRACT FROM AUTHOR]

This study develops a short-term planning model to determine the bidding curves on a day-ahead market for a price-maker retailer with flexible power demand. It concerns the interactions between the spot price and the flexible demand. Both conditional value-at-risk and volume deviation risk are taken into account. A numerical study using the data from the Nordic electricity market is performed to investigate the influence of risk factors on the retailer's profit, risk levels, average spot price, and total consumption. Three types of price elasticity are compared to show that the retailer can benefit from the flexibility in demand side in some cases. The flexibility also leads to lower spot prices so that the customers in real-time price-based demand response can face a lower electricity price for per-unit power consumption. [ABSTRACT FROM AUTHOR]

This paper deals with the market-bidding problem of a cluster of price-responsive consumers of electricity. We develop an inverse optimization scheme that, recast as a bilevel programming problem, uses price-consumption data to estimate the complex market bid that best captures the price-response of the cluster. The complex market bid is defined as a series of marginal utility functions plus some constraints on demand, such as maximum pick-up and drop-off rates. The proposed modeling approach also leverages information on exogenous factors that may influence the consumption behavior of the cluster, e.g., weather conditions and calendar effects. We test the proposed methodology for a particular application: forecasting the power consumption of a small aggregation of households that took part in the Olympic Peninsula project. Results show that the price-sensitive consumption of the cluster of flexible loads can be largely captured in the form of a complex market bid, so that this could be ultimately used for the cluster to participate in the wholesale electricity market. [ABSTRACT FROM PUBLISHER]

This paper investigates joint scheduling problem of large-scale smart appliances and batteries (e.g., in a smart building), to minimize electricity payment, user's dissatisfaction and battery loss under kinds of constraints. Due to the binary nature of charge and discharge states of battery, this problem is formulated as a constrained mixed-integer nonlinear program. In order to solve it efficiently, a distributed mixed optimization approach is proposed. First, Lagrangian relaxation is applied to decompose the original problem into two sets of subproblems, each of which corresponds to scheduling on appliance/battery. Then, the battery scheduling subproblem is formulated as a mixed-integer linear program and tackled by Benders decomposition. The main advantages of the proposed approach are the distributed implementation and low computational complexity, as shown by simulations. [ABSTRACT FROM PUBLISHER]

This paper presents the design and evaluation of an effective market-clearing scheme for trading demand response (DR) in a deregulated power system. The proposed scheme is called demand response exchange (DRX), in which DR is treated as a public good to be exchanged between two groups of participating agents, namely DR buyers and DR sellers. While buyers require DR and are willing to pay for it, sellers have the capacity to curtail customer loads to supply DR on request. The DRX market clearing scheme uses Walrasian auctions, where agents update their DR quantity bids in response to prices adjusted by the market operator. This auction is repeated iteratively until market equilibrium is obtained at the point where the market outcome is proven to be Pareto optimal. The proposed scheme is tested on a small power system and its effectiveness substantiated. [ABSTRACT FROM PUBLISHER]

We address the generation investment problem faced by a strategic power producer and consider a detailed description of the uncertain parameters involved, namely, rival producer investment and market offering, and demand growth. To identify optimal investment decisions, we consider a target year and propose a bilevel model whose upper-level problem determines investment and offering decisions to maximize expected profit, and whose many lower-level problems represent market clearing conditions per demand block and scenario. Since the producer total expected profit is sufficiently convex with respect to investment decisions, a Benders' decomposition approach is proposed that results in a tractable formulation even if hundred of scenarios are used to describe uncertain parameters. Extensive numerical simulations based on realistic case studies show the good performance of the proposed decomposition approach. [ABSTRACT FROM PUBLISHER]