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Exploiting Flexibility in Coupled Electricity and Natural Gas Markets: A Price-Based Approach
Abstract
Natural gas-fired power plants (NGFPPs) are considered a highly flexible component of the energy system and can facilitate the large-scale integration of intermittent renewable generation. Therefore, it is necessary to improve the coordination between electric power and natural gas systems. Considering a market-based coupling of these systems, we introduce a decision support tool that increases market efficiency in the current setup where day-ahead and balancing markets are cleared sequentially. The proposed approach relies on the optimal adjustment of natural gas price to modify the scheduling of power plants and reveals the necessary flexibility to handle stochastic renewable production. An essential property of this price-based approach is that it guarantees no financial imbalance (deficit or surplus) for the system operator at the day-ahead stage. Our analysis shows that the proposed mechanism reduces the expected system cost and efficiently accommodates high shares of renewables.
... Several works in the literature have investigated different coordination levels of power and natural gas systems in terms of short-term operational integration, proving the advantages of such coupling for the whole system, see [6]- [9]. A full coupling (co-optimization) of the two energy systems is not compatible with the current market regulations in most countries which usually require decoupled operations on different temporal scales [10]. ...
... This tractable MILP formulation of bidirectional gas flow model accounts for linepack flexibility. Instead of relaxing the solution space described by the Weymouth equation into a SOC as in (2a), the approach in [6] defines a number of planes tangent to the cone described by (1k), see Fig. 2. Each equality constraint (1k) is replaced by a set of linear inequalities (9), that linearly approximate the Weymouth equation around fixed pressure points P R m,v , P R u,v , ∀(m, u) ∈ Z, v ∈ V: ...
Utilizing operational flexibility from natural gas networks can foster the integration of uncertain and variable renewable power production. We model a combined power and natural gas dispatch to reveal the maximum potential of linepack, i.e., energy storage in the pipelines, as a source of flexibility for the power system. The natural gas flow dynamics are approximated by a combination of steady-state equations and varying incoming and outgoing flows in the pipelines to account for both natural gas transport and linepack. This steady-state natural gas flow results in a nonlinear and nonconvex formulation. To cope with the computational challenges, we explore convex quadratic relaxations and linear approximations. We propose a novel mixed-integer second-order cone formulation including McCormick relaxations to model the bidirectional natural gas flow accounting for linepack. Flexibility is quantified in terms of system cost compared to a dispatch model that either neglects linepack or assumes infinite storage capability.
... In [9], one joint ISO for electricity and natural gas is proposed to increase the overall market efficiency and reduce the total operational cost by satisfying the gas demand of NGFPPs while shedding gas offtakes from other sectors. A similar structure of the joint ISO is also adopted in [10], and the proposed price-based approach provides necessary incentive to adjust NGFPPs' schedule. These examples reveal that the improvement on the gas price flexibility will be useful for the enhancement of market efficiency in both systems. ...
... The pool-based market mechanism is applied to both the electricity and natural gas markets here. Unlike the one joint ISO in [9]- [10], these two markets remain separate, with respective ISOs executing clearing processes independently, which are consistent with the current governance structure. Instead of the reformulation with KKT conditions in [11]- [12], the CE algorithm is adapted to acquire equilibrium of the coupled markets. ...
With the increasing utilization of natural gas-fired power plants, efficiency and even reliability challenges on both industries arise from the growing reliance on natural gas as fuel in the electricity sector and the increasing demand fluctuation in the natural gas sector. Following a brief review of the discontinuities, improvements are discussed to coping with the desired timely and cost-effectively gas supply in the electricity day-ahead market scheduling. Inspired by its successful implantation in the electricity market, the locational marginal pricing mechanism is applied to the natural gas day-ahead market. Moreover, the bidding strategy for general gas demand utilities is provided while considering the potential applicability of demand response which is also practiced in the electricity market. The formulations are represented as bilevel problems of each corresponding market, of which upper level problems maximize the participants’ profit while the lower level problem renders the market clearing with schedules and locational marginal prices. Then, a coevolutionary algorithm is proposed to find the equilibrium of the two coupled markets with integrated participants. Results show that the proposed methodology with synchronized intraday time intervals and demand response abilities could provide higher efficiency for the natural gas market, which will help the electricity market better coordinated to provide flatter price signals while scheduling more renewable energy sources.
... Ordoudis et al. [91] proposed an insightful price-based (PB) stochastic bi-level approach, considering a coupled electricity and natural gas market, where gas consumption and price offered by NGFPPs, constitute the coordination parameters between the two market setups. The recommended optimization mechanism ensures that ISO will form a financial day-ahead equilibrium, whereas may encounter an implicit real-time shortage or surplus. ...
In recent years, the ever-increasing research interest in various aspects of the electricity pool-based markets has generated a plethora of complementarity-based approaches to determine participating agents’ optimal offering/bidding strategies and model players’ interactions. In particular, the integration of multiple and diversified market agents, such as conventional generation companies, renewable energy sources, electricity storage facilities and agents with a mixed generation portfolio has instigated significant competition, as each player attempts to establish their market dominance and realize substantial financial benefits. The employment of complementarity modelling approaches can also prove beneficial for the optimal coordination of the electricity and natural gas market coupling. Linear and nonlinear programming as well as complementarity modelling, mainly in the form of mathematical programs with equilibrium constraints (MPECs), equilibrium programs with equilibrium constraints (EPECs) and conjectural variations models (CV) have been widely employed to provide effective market clearing mechanisms, enhance agents’ decision-making process and allow them to exert market power, under perfect and imperfect competition and various market settlements. This work first introduces the theoretical concepts that regulate the majority of contemporary competitive electricity markets. It then presents a comprehensive review of recent advances related to complementarity-based modelling methodologies and their implementation in current competitive electricity pool-based markets applications.
... From a market perspective, blending renewable hydrogen and synthetic methane with natural gas creates another link between the electricity and the natural gas markets. So far, gas-fired power plants are the only interface between the power and gas systems [27]. Several studies have assessed the interaction between gas and power markets using market models (e.g. ...
We perform a model-based analysis of the impact of a renewable hydrogen quota on EU gas and electricity markets. By comparing a scenario in which a renewable hydrogen quota with tradable certificates is imposed on final gas consumption in the sectors of the economy outside the EU emissions trading system with a reference scenario without a quota, we assess price, quantity and welfare effects. Our model simulations show that the hydrogen quota leads to a significant expansion in renewable electricity generating capacity to produce renewable hydrogen and synthetic methane with power-to-gas technologies. On the electricity market, the price increases substantially, rising by up to 12% – mostly due to increasing emission allowance prices – leading to a higher surplus for power producers. The quota’s primary beneficiaries in the power sector are renewable energy producers. On the gas market, the quota leads to a small decrease in prices (by a maximum of -3%) and gas producer surpluses. Quota obliged gas consumers, mainly households, commercial and small industrial consumers, carry the largest part of the burden associated with the obligation. Overall, the quota leads to the redistribution of welfare from these consumers to renewable electricity generators and power-to-gas producers and a significant decline in total welfare.
... According to [22], none of the electricity markets is synchronised with natural gas markets in the U.S. As the misalignment of these two markets brings challenges and inconvenience to planning and operation of the integrated energy system, lots of efforts have been done to adjust electricity day and gas day [15]. Meanwhile, many types of research [23][24][25] focus on the scheduling of the integrated energy system in synchronous market mechanisms. However, a few papers are available on the asynchronous markets, which are more practical and complicated. ...
The growing utilisation of natural gas and renewable energy resources brings more challenges to generation expansion planning problems. Short‐term operational constraints are equally important for long‐term capacity planning. This work studies the interdependence between electricity and natural gas systems and presents a combined market mechanism that allows two‐stage energy trading and planning based on asynchronous electricity and gas markets. In the first stage, the gas market is cleared with the objective of maximum social welfare (lower level), at the same time obtaining the optimal strategies offered by gas producers and gas‐fired units (upper level). In the second stage, generation companies and consumer companies aim to maximise their corresponding profits in the planning horizon (upper level), and electricity market is cleared on principle of maximum social welfare (lower level). Then, the authors develop a modified alternative direction method of multiplier algorithm to solve the two‐stage nested bilevel model. To improve operational flexibility of expansion plans, uncertainties of renewable energy generation and integrated demand response are also included and analysed in different risk scenarios. Case studies validate the effectiveness of the proposed methodology.
... Similar challenges have been tackled in the literature regarding the coordination of electricity and natural gas systems. Ordoudis et al. (2017) propose a bilevel optimization model for coordination of natural gas and electricity markets, relying on the optimal adjustment of natural gas prices to improve the scheduling of power plants. Byeon & Van Hentenryck (2019b) propose a gas-aware unit commitment model using a trilevel optimization, which accounts for the impact of gas prices on the profitability of the bids of gas-fired power plants in the electricity market through the so-called bid-validity constraints. ...
The large penetration of stochastic and non-dispatchable renewable energy sources increases the need for operational flexibility in power systems. Flexibility can be unlocked by aligning the existing interactions and synergies between heat and power systems. However, in the current sequential order of heat and electricity market clearings, the heat market is myopic to its interactions with the electricity market. This paper designs a heat market, aimed at achieving the optimal coordination of heat and power systems while respecting the current market regulations. The proposed electricity-aware heat market yields a soft coordination between heat and power systems by endogenously modeling their interactions in the day-ahead heat market clearing. The proposed market framework requires to solve a hierarchical optimization problem under uncertainty, which can be computationally challenging in large-scale energy systems with many scenarios. To resolve this potential scalability issue, this paper develops an augmented regularized Benders decomposition algorithm. The performance of the proposed market framework is compared against the fully integrated and sequential market frameworks using an ex-post out-of-sample simulation. This comparison reveals that there is a significant room for improvement in the cost-effective operation of the overall energy system. In particular, the proposed electricity-aware heat market framework provides a trade-off between the sequential and fully integrated market frameworks by significantly reducing the inefficiencies in both heat and electricity systems while respecting the current sequence of clearing heat and electricity markets.
... However, this formulation would unnecessarily complicate the model and increase its computational burden. For similar applications of bilevel programming and further discussion on its conceptual aspects, the reader is referred to [40,43,49]. ...
The increasing shares of stochastic renewables bring higher uncertainty in power system operation and underline the need for optimal utilization of flexibility. However, the European market structure that separates energy and reserve capacity trading is prone to inefficient utilization of flexible assets, such as the HVDC interconnections, since their capacity has to be ex-ante allocated between these services. Stochastic programming models that co-optimize day-ahead energy schedules with reserve procurement and dispatch, provide endogenously the optimal transmission allocation in terms of minimum expected system cost. However, this perfect temporal coordination of trading floors cannot be attained in practice under the existing market design. To this end, we propose a decision-support tool that enables an implicit temporal coupling of the different trading floors using as control parameters the inter-regional transmission capacity allocation between energy and reserves and the area reserve requirements. The proposed method is formulated as a stochastic bilevel program and cast as mixed-integer linear programming problem, which can be efficiently solved using a Benders decomposition approach that improves computational tractability. This model bears the anticipativity features of a transmission allocation model based on a pure stochastic programming formulation, while being compatible with the current market structure. Our analysis shows that the proposed mechanism reduces the expected system cost and thus can facilitate the large-scale integration of intermittent renewables.
Transport and trade of gas are decoupled after the liberalization of the European gasmarkets, which are now organized as so-called entry-exit systems. At the core of thismarket system are bookings and nominations, two special capacity-right contractsthat grant traders access to the gas network. The latter is operated by a separateentity, known as the transmission system operator (TSO), who is in charge of thetransport of gas from entry to exit nodes. In the mid to long term, traders signa booking contract with the TSO to obtain injection and withdrawal capacities atentry and exit nodes, respectively. On a day-ahead basis, they then nominate withinthese booked capacities a balanced load flow of the planned amounts of gas to beinjected into and withdrawn from the network the next day. The key property isthat by signing a booking contract, the TSO is obliged to guarantee transportabilityfor all balanced load flows in compliance with the booked capacities. To assess thefeasibility of a booking, it is therefore necessary to check the feasibility of infinitelymany nominations. As a result, deciding if a booking is feasible is a challengingmathematical problem, which we investigate in this dissertation.Our results range from passive networks, consisting of pipes only, to activenetworks, containing controllable elements to influence gas flows. Since the study ofthe latter naturally leads to a bilevel framework, we first consider some more generalproperties of bilevel optimization. For the case of linear bilevel optimization, weconsider the hardness of validating the correctness of big-M s often used in solvingthese problems via a single-level reformulation. We also derive a family of validinequalities to be used in a bilevel-tailored branch-and-cut algorithm as a big-M -freealternative.We then turn to the study of feasible bookings. First, we present our results onpassive networks, for which bilevel approaches are not required. A characterization offeasible bookings on passive networks is derived in terms of a finite set of nominations.While computing these nominations is a difficult task in general, we present polynomialcomplexity results for the special cases of tree-shaped or single-cycle passive networks.Finally, we consider networks with linearly modeled active elements. After obtaininga bilevel optimization model that allows us to determine the feasibility of a bookingin this case, we derive various single-level reformulations to solve the problem. Inaddition, we obtain novel characterizations of feasible bookings on active networks,which generalize our characterization in the passive case. The performance of thesevarious approaches is compared in a case study on two networks from the literature,one of which is a simplified version of the Greek gas network.
This chapter introduces real-time operation based on model predictive control (MPC) for integrated energy systems. The integrated energy system utilizes power-to-gas, the electric boiler, and the heat pump, as well as thermal energy and gas storages. MPC is implemented as a rolling horizon scheme that jointly optimizes the electric power, natural gas, and district heating systems to obtain higher flexibility, and penalizes the deviation from the prescheduled values. An online learning method updates forecasts with high accuracy and decreases the computational burden compared to the conventional method. Overall, online MPC updates and calculates the optimal control sequence for the prediction horizon at every timestep. The online learning method combined with MPC ensures a decrease in operation costs over a prediction horizon and an increase in the flexibility of the entire system. The economic and energy efficiency improve compared to the traditional RT scheduling by predicting the future information of the system.
This chapter presents a two-stage stochastic day-ahead scheduling of integrated energy systems. The first stage represents the day-ahead market, whereas the second stage accommodates the wind power uncertainties. Wind power uncertainties are represented through a set of scenarios generated by a proposed scenario generation method. The proposed scenario generation method takes into account temporal correlation of wind power and presents a reliable input to the second stage. The stochastic programming approach shows minimum total expected system cost. Furthermore, integration and coordination of several energy subsystems provide higher utilization of wind power and decrease in total expected system costs. Linking units, such as power-to-gas, can convert excess electricity to gas and decrease the wind spillage. Hence, the system flexibility and wind power utilization increase by integration of natural gas, electric power, and district heating systems through linking components. The total expected system cost decreases due to coordinated scheduling of energy and reserves.
Electric power system reform and innovative energy transaction policies have become increasingly common. In view of the fact that research on today’s multi-energy coupled market have not yet incorporated multi-energy substitution characteristics on the supply and demand sides, nor the coordination between regional and user level markets. A joint energy market was developed in this study for the procurement of local integrated energy system services considering demand flexibility. The transaction mechanisms of the regional level energy coupled market and user level Peer-to-Peer (P2P) distributed transaction market were determined accordingly. The multi-energy and complementary regional energy market was coupled with the user-level distributed transaction market that considering flexible demand response characteristics to establish a regional integrated energy system joint market. The electricity, gas, and heat markets in the regional market were modeled separately, then a joint clearing mechanism of the energy coupled market was established. An autonomous scheduling model for distributed market participants at the user level and a P2P transaction mechanism considering multi-agent non-cooperative game was proposed. The bidding behaviors of different energy operators in the regional level market were simulated to assess the influencing factors. The economic and technical benefits of P2P transactions at the user level as remitted to market participants were quantitatively determined with specific indexes. The correlations and interactions between the region level and user level markets were also analyzed.
Integration of the electric power system, natural gas system, and district heating system can reduce the operational cost and improve the utilization of renewable energy sources. The day-ahead schedule for the optimal operation of the integrated energy system may not be economically optimal in real-time due to the prediction errors of multiple uncertainty sources. To balance the real-time prediction errors economically, this paper proposes a model predictive control (MPC) based real-time scheduling strategy to optimize the real-time operation of the integrated energy system, which makes real-time operational decisions based on the measured state of the system and future information of uncertainties. In the MPC based real-time scheduling, the penalty for the deviation between the day-ahead and real-time schedules is considered to minimize the regulation cost. In addition, multiple uncertainty sources are taken into account. An online learning method is utilized in MPC to predict the future information of these uncertainties. Besides, the power-to-x technology and thermal energy and gas storage devices are considered to improve the capability of the system to balance these uncertainties. The simulation results show that the MPC based real-time scheduling outperforms the traditional real-time scheduling on economic efficiency and wind power utilization.
In many parts of the world, electric power systems have seen a significant shift toward generation from renewable energy and natural gas. Because of their ability to flexibly adjust power generation in real time, gas-fired power plants are frequently seen as the perfect partner for variable renewable generation. However, this reliance on gas generation increases interdependence and propagates uncertainty between power grids and gas pipelines and brings coordination and uncertainty management challenges. To address these issues, we propose an uncertainty management framework for uncertain, but bounded gas consumption by gas-fired power plants. The admissible ranges are computed based on a joint optimization problem for the combined gas and electricity networks, which involves chance-constrained scheduling for the electric grid and a novel robust optimization formulation for the natural-gas network. This formulation ensures feasibility of the integrated system with a high probability, while providing a tractable numerical formulation. A key advance with respect to existing methods is that our method is based on a physically accurate, validated model for transient gas pipeline flows. Our case study benchmarks our proposed formulation against methods that ignore how reserve activation impacts the fuel use of gas power plants and only consider predetermined gas consumption. The results demonstrate the importance of considering uncertainty to avoid operating constraint violations and curtailment of gas to the generators.
In many parts of the world, electric power systems have seen a significant shift towards generation from renewable energy and natural gas. Because of their ability to flexibly adjust power generation in real time, gas-fired power plants are frequently seen as the perfect partner for variable renewable generation. However, this reliance on gas generation increases interdependence and propagates uncertainty between power grids and gas pipelines, and brings coordination and uncertainty management challenges. To address these issues, we propose an uncertainty management framework for uncertain, but bounded gas consumption by gas-fired power plants. The admissible ranges are computed based on a joint optimization problem for the combined gas and electricity networks, which involves chance-constrained scheduling for the electric grid and a novel robust optimization formulation for the natural gas network. This formulation ensures feasibility of the integrated system with a high probability, while providing a tractable numerical formulation. A key advance with respect to existing methods is that our method is based on a physically accurate, validated model for transient gas pipeline flows. Our case study benchmarks our proposed formulation against methods that ignore how reserve activation impacts the fuel use of gas power plants, and only consider predetermined gas consumption. The results demonstrate the importance of considering uncertainty to avoid operating constraint violations and curtailment of gas to the generators.
Secure operation of the power system is challenged by the high level of uncertainty and fluctuation introduced by renewable energy sources. More flexibility is needed to cope with the uncertainty and improve the utilization of renewable energy. A prominent solution to provide flexibility, and simultaneously increase the efficiency of the system, is the integration of different energy sectors. This paper proposes a two-stage stochastic scheduling scheme of an integrated multi-energy system, which considers the wind power uncertainty and the synergy of different energy sectors to achieve the optimal economic operation of the whole system with minimum curtailment of wind power. In the first stage, energy and reserve scheduling of generating units is performed, while accommodation of wind power production is realized through reserves in the second stage. In the proposed scheme, the electric power system, natural gas system, and district heating system are coordinated to achieve more flexibility, both in the day-ahead and real-time stage. The stochasticity of the wind power uncertainty is represented by realistic scenarios with corresponding probabilities, which are obtained from a scenario generation algorithm based on historical observations taking into account the temporal correlation of wind power. The simulation results on a small-scale test system show that both, the economic efficiency and wind power utilization, have been improved with more flexibility and more reliable scenario set. It is shown, that the total system cost is reduced and reserves are optimized.
Recent changes in the fuel mix for electricity generation and, in particular, the increase in Gas-Fueled Power Plants (GFPP), have created significant interdependencies between the electrical power and natural gas transmission systems. However, despite their physical and economic couplings, these networks are still operated independently, with asynchronous market mechanisms. This mode of operation may lead to significant economic and reliability risks in congested environments as revealed by the 2014 polar vortex event experienced by the northeastern United States. To mitigate these risks, while preserving the current structure of the markets, this paper explores the idea of introducing gas network awareness into the standard unit commitment model. Under the assumption that the power system operator has some (or full) knowledge of gas demand forecast and the gas network, the paper proposes a tri-level mathematical program where natural gas zonal prices are given by the dual solutions of natural-gas flux conservation constraints and commitment decisions are subject to bid-validity constraints that ensure the economic viability of the committed GFPPs. This tri-level program can be reformulated as a single-level Mixed-Integer Second-Order Cone program which can then be solved using a dedicated Benders decomposition. The approach is validated on a case study for the Northeastern United States
[1]
that can reproduce the gas and electricity price spikes experienced during the early winter of 2014. The results on the case study demonstrate that gas awareness in unit commitment is instrumental in avoiding the peaks in electricity prices while keeping the gas prices to reasonable levels.
In this paper, the coordination of constrained electricity and natural gas infrastructures is considered for firming the variability of wind energy in electric power systems. The stochastic security-constrained unit commitment is applied for minimizing the expected operation cost in the day-ahead scheduling of power grid. The low cost and sustainable wind energy could substitute natural gas-fired units, which are constrained by fuel availability and emission. Also, the flexibility and quick ramping capability of natural gas units could firm the variability of wind energy. The electricity and natural gas network constraints are considered in the proposed model (referred to as EGTran) and Benders decomposition is adopted to check the natural gas network feasibility. The autoregressive moving average (ARMA) time-series model is used to simulate wind speed forecast errors in multiple Monte Carlo scenarios. Illustrative examples demonstrate the effectiveness of EGTran for firming the variable wind energy by coordinating the constrained electricity and natural gas delivery systems.
We discuss a stochastic-programming-based method for scheduling electric power gener- ation subject to uncertainty. Such uncertainty may arise from either imperfect forecasting or moment-to-moment fluctuations, and on either the supply or the demand side. The method gives a system of locational marginal prices which reflect the uncertainty, and these may be used in a market settlement scheme in which payment is for energy only. We show that this scheme is revenue-adequate in expectation.
We propose a two-stage mixed-integer linear stochastic optimization model to analyze the scheduling of electricity-production units under natural gas-supply uncertainty due to pipeline congestion and natural gas-price variability. The first stage of this stochastic optimization model represents the day-ahead scheduling (i.e., unit commitment) stage, while the second stage represents actual real-time operations through a number of scenarios. We use this model to analyze the effect on unit commitment and dispatch of two types of natural gas-supply conditions. First, we analyze a case involving low-cost natural gas supply with natural gas-transmission issues related to potential gas-pipeline congestion. We then examine a case involving higher-cost natural gas, which is used solely to attain feasibility with fast-ramping events. The first case mimics situations in the ISO New England system, in which relatively low-cost natural gas supply is uncertain in cold-weather conditions due to natural gas-transmission bottlenecks. The second case is reminiscent of situations in the California ISO system, in which relatively expensive but flexible natural gas-fired units need to be used to handle rapid changes in net demand in the early mornings and late afternoons.
The extensive installation of gas-fired power plants in many parts of the world has led electric systems to depend heavily on reliable gas supplies. The use of gas-fired generators for peak load and reserve provision causes high intra-day variability in withdrawals from high pressure gas transmission systems. Such variability can lead to gas price fluctuations and supply disruptions that affect electric generator dispatch, electricity prices, and threaten the security of power systems and gas pipelines. These infrastructures function on vastly different spatiotemporal scales, which prevents current practices for separate operations and market clearing from being coordinated. In this paper, we apply new techniques for control of dynamic gas flows on pipeline networks to examine day-ahead scheduling of electric generator dispatch and gas compressor operation for different levels of integration, spanning from separate forecasting and simulation to combined optimal control. We formulate multiple coordination scenarios and develop tractable, physically accurate computational implementations. These scenarios are compared using an integrated model of test networks for power and gas systems with 24 nodes and 24 pipes, respectively, which are coupled through gas-fired generators. The analysis quantifies the economic efficiency and security benefits of gas-electric coordination and dynamic gas system operation.
This article provides an overview of the European Union (EU) electricity and natural gas sectors by focusing on the specific case of mainland Spain. The integration of renewable production has created a strong link between the operations of the electricity and natural gas systems. This calls for a coordinated gas and electricity operation and for the coordinated long-term planning of both electricity and natural gas facilities.
The significant growth in gas-fired units worldwide
has increased the grade of interdependency between power and
natural gas networks. Since these units are usually required to
ramp up during the peak and backup intermittent renewable
generation and contingencies, the power system tends to demand
more flexibility and reliability from the gas system. This paper
contributes with a novel mixed-integer linear programing (MILP)
formulation that couples power and gas networks taking into
account the gas traveling velocity and compressibility. As a result,
the model accounts for the gas adequacy needed to assure the
power system reliability in the short term. The robustness of the
MILP formulation allows guaranteeing global optimality within
predefined tolerances. Case studies integrate the IEEE 24-bus
system and Belgian high-calorific gas network for validating the
formulation.
This article addresses the functioning of capacity remuneration mechanisms (CRMs) in an integrated European electricity market featuring a high share of intermittent renewable energy sources. We first highlight the close ties between flexibility provision and generation adequacy, and explain why these two issues must be considered concomitantly when developing CRMs. We then show that while Member States have different needs, addressing security of supply in a purely national way will be expensive. We finally identify three prerequisites for a workable Europeanization of national generation adequacy mechanisms: a consistent assessment of adequacy needs and cross-border resources, a dedicated method to allocate risks and remuneration of cross-border resources contribution, and a definition of rights over the system resources at times of extreme scarcity.
In this paper, we consider an electricity market that consists of a day-ahead and a balancing settlement, and includes a number of stochastic producers. We first introduce two reference procedures for scheduling and pricing energy in the day-ahead market: on the one hand, a conventional network-constrained auction purely based on the least-cost merit order, where stochastic generation enters with its expected production and a low marginal cost; on the other, a counterfactual auction that also accounts for the projected balancing costs using stochastic programming. Although the stochastic clearing procedure attains higher market efficiency in expectation than the conventional day-ahead auction, it suffers from fundamental drawbacks with a view to its practical implementation. In particular, it requires flexible producers (those that make up for the lack or surplus of stochastic generation) to accept losses in some scenarios. Using a bilevel programming framework, we then show that the conventional auction, if combined with a suitable day-ahead dispatch of stochastic producers (generally different from their expected production), can substantially increase market efficiency and emulate the advantageous features of the stochastic optimization ideal, while avoiding its major pitfalls.
A two-node power system serves as both an illustrative example and a proof of concept. Finally, a more realistic case study highlights the main advantages of a smart day-ahead dispatch of stochastic producers.
Until recently Chairman of the Massachusetts Department of Public Utilities, Paul J. Hibbard is a Vice President at Analysis Group's Boston office. At the DPU, Hibbard led an aggressive policy agenda to advance gas and electric energy efficiency and renewable resources, oversaw the institution of revenue decoupling for natural gas and electric utilities in the state, coordinated regional efforts in the development of energy resources and associated infrastructure, and promoted the administration of fair and efficient transmission pricing models in regional and national contexts. In his dozen-year consulting career before and after the DPU, he has worked on technical issues related to market design and analysis, economic and environmental regulatory policy, and infrastructure planning and siting in the electric and gas industries. As a policymaker and energy expert, Hibbard has testified extensively before legislative and regulatory agencies.
Electronic companion for paper: Exploiting flexibility in coupled electricity and natural gas markets: A price-based approach
- C Ordoudis
- S Delikaraoglou
- P Pinson
- J Kazempour
C. Ordoudis, S. Delikaraoglou, P. Pinson and J. Kazempour, "Electronic companion for paper: Exploiting flexibility in coupled electricity and natural gas markets: A price-based approach", [Online],
http://doi.org/10.5281/zenodo.376307.
Data for stochastic multiperiod optimal power flow problem
- W Bukhsh
W. Bukhsh, "Data for stochastic multiperiod optimal power flow problem," Website, Mar. 2017, https://sites.google.com/site/datasmopf/.