Search

A linear and convex model has been recently presented for the operation of battery energy storage units that does not explicitly prevent their simultaneous charge and discharge in the problem formulation. This letter claims that the model in such a literature contribution is capable of ensuring the physical realizability of the battery dispatch only when nondiscrete decisions are considered in the optimization problem. This claim is demonstrated through the solution of two instances of a simple unit-commitment-based example comprising two generating units, one battery energy storage unit, and two time periods. [ABSTRACT FROM AUTHOR]

Recently, several authors have reported specific criteria to support the widely accepted use of a convex operational model for bulk energy storage in the formulation of power system operation and planning problems involving binary variables. Using two counterexamples, we show that this modeling simplification may give rise to impractical solutions featuring simultaneous charging and discharging, even though the recently published criteria are satisfied. Thus, both counterexamples invalidate those criteria and demonstrate that the customarily used convex model may be unsuitable for the precise incorporation of bulk storage in such instances of mixed-integer programming. [ABSTRACT FROM AUTHOR]

• MES was proposed as a route to convert CO 2 to commodities. • Performance indicators of MES are plateauing and far from economic competitiveness. • Effective water electrolysis in microbial-compatible electrolytes is challenging. • Severe trade-off between production rate and energy conversion efficiency. • Improvement of MES, applicability and possible directions are discussed. Microbial electrosynthesis (MES) is an electrochemical process used to drive microbial metabolism for bio-production, such as the reduction of CO 2 into industrially relevant organic products as an alternative to current fossil-fuel-derived commodities. After a decade of research on MES from CO 2 , figures of merit have increased significantly but are plateauing yet far from those expected to allow competitiveness for synthesis of commodity chemicals. Here we discuss the substantial technological shortcomings still associated with MES and evoke possible ways to mitigate them. It appears particularly challenging to obtain both relevant production rates (driven by high current densities) and energy conversion efficiency (i.e. low cell voltage) in microbial-compatible electrolytes. More competitive processes could arise by decoupling effective abiotic electroreductions (e.g. CO 2 to CO or ethanol; H 2 evolution) with subsequent fermentation processes. [ABSTRACT FROM AUTHOR]

In the restructured power industry, bulk energy storage may play a crucial role to provide the flexibility required by system operators to cater for the unprecedented levels of uncertainty. Within the context of co-optimized electricity markets for energy and reserves under wind uncertainty, this paper addresses the incorporation of bulk energy storage units in day-ahead network-constrained energy and reserve scheduling. A novel two-stage robust optimization approach is presented whereby the nonconvex and time-coupled operation of storage devices is precisely modeled while accounting for the anticipativity of the two-stage setting. The resulting robust counterpart is cast as a mixed-integer trilevel program with lower-level binary variables. In order to address the nonconvexity of the recourse problem, this paper proposes the application of an exact nested column-and-constraint generation algorithm. Numerical results illustrate the effective performance of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents the numerical analysis of the bilevel programming approach for revenue- and network-constrained market clearing developed in its companion paper. The impact of minimum revenue conditions and minimum declared profits on generation and consumption levels as well as on locational marginal prices for energy is examined in detail through three case studies. First, the results from an illustrative example including minimum revenue conditions are comprehensively analyzed. The second case study is based on the IEEE Reliability Test System and considers minimum declared profits. In the third case study, a modified version of the IEEE 118-bus system is tested while accounting for minimum revenue conditions. In addition, the computational behavior of the proposed approach is illustrated with several case studies including the IEEE 300-bus system. Numerical results show the effectiveness of the proposed approach to handle revenue constraints as well as its superiority over the heuristic currently implemented in the Iberian electricity market. Moreover, simulations reveal that, unlike previous works in the literature, generation revenue constraints can be precisely incorporated in day-ahead market clearing while explicitly considering the standard economic-dispatch-based marginal pricing scheme and without requiring price uplifts. [ABSTRACT FROM AUTHOR]

The first of this two-paper series addresses a practical day-ahead auction model, where generation revenue constraints are explicitly incorporated in the problem formulation, as routinely done in several national electricity markets across Europe. The revenue-constrained market-clearing procedure includes the effect of the transmission network, inter-temporal constraints associated with generation scheduling, demand-side bidding, and marginal pricing. This auction design is an instance of price-based market clearing which features two major complicating factors. First, locational marginal prices become decision variables of the optimization process. In addition, producer revenues are formulated as bilinear and highly nonconvex products of power outputs and market-clearing prices. The resulting problem is formulated as a mixed-integer nonlinear bilevel program with bilinear terms for which available solution techniques rely on heuristics, approximations, or modeling simplifications. This paper presents a novel and exact methodology whereby the original problem is recast as an equivalent single-level mixed-integer linear program. As a consequence, finite convergence to optimality is guaranteed and the use of standard commercial software is allowed. The proposed transformation is based on duality theory of linear programming, Karush-Kuhn-Tucker optimality conditions, and integer algebra results. In the second part of this two-paper series, numerical results from several case studies illustrate the effective performance of the proposed solution approach. [ABSTRACT FROM AUTHOR]

This paper presents an alternative day-ahead auction based on consumer payment minimization for pool-based electricity markets. This auction is an instance of price-based market clearing wherein market-clearing prices are explicitly modeled as decision variables of the optimization. The auction design includes network constraints, inter-temporal constraints associated with generation scheduling, and marginal pricing. Hence, consumer payment is expressed in terms of locational marginal prices. The proposed solution approach is based on bilevel programming. In the upper-level optimization, generation is scheduled with the goal of minimizing the total consumer payment while taking into account that locational marginal prices are determined by a multiperiod optimal power flow in the lower level. In this bilevel programming setting, locational marginal prices are the Lagrange multipliers or dual variables associated with the nodal power balance equations of the lower-level problem. The resulting mixed-integer linear bilevel program is transformed into an equivalent single-level mixed-integer linear program suitable for efficient off-the-shelf software. This transformation relies on the application of results from duality theory of linear programming and integer algebra. The proposed methodology has been successfully applied to several test systems including the IEEE 118-bus system. Numerical results have been compared with those obtained from declared social welfare maximization. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of price multiplicity in network-constrained pool-based electricity markets under marginal pricing. This problem consists in the existence of multiple vectors of locational marginal prices for the same optimal market-clearing dispatch solution. As a consequence, delicate conflicts of interest may arise among market participants. Price multiplicity may take place even in auction designs based on convex formulations for the market-clearing procedure. An effective duality-based solution approach is proposed in this paper to handle the issue of price multiplicity. Once the optimal market-clearing dispatch is known, we propose the subsequent solution of a simple pricing problem in order to determine the vector of locational marginal prices. Unlike previously reported methodologies, the proposed approach presents two salient features: 1) it is based on sound mathematical programming, and 2) it is computationally inexpensive since it relies on the solution of a linear program. Numerical results are provided to illustrate the performance of the proposed tool. [ABSTRACT FROM PUBLISHER]

Investigates the self-scheduling problem faced by a price-maker in achieving maximum profit in a pool-based electricity market. Description of the price quota curve of a price maker; Functions of the electricity market; Details on the case study of an electricity market with one price maker producer.

This paper presents a new formulation for the preventive security-constrained unit commitment problem modeling N-k line outages and transmission losses. The pre- and post-contingency transmission constraints, representing N-k line outages, are explicitly included by using generalized generation distribution factors. To account for security, a contingency selection procedure based on line outage distribution factors finds a list of worst-case contingencies. Transmission losses are incorporated using piecewise linear expressions. The proposed model is formulated as an instance of mixed-integer linear programming. The effectiveness of the proposed approach is illustrated with the IEEE 57-bus system and the 1,354-bus portion of the European transmission system. As empirically evidenced, the explicit consideration of N-k line outages and transmission losses leads to different decisions in the generation scheduling and dispatch, ensuring secure power system operation. [ABSTRACT FROM AUTHOR]

• Integrated fixed-film activated sludge tested for shortcut nitrogen removal. • Combining strategies selectively suppressed activity nitrite oxidizing bacteria. • Flocs acted as nitrite source, while carriers were nitrite sinks. • Optimal nitrite source: low, but sufficient aerobic floc retention time (±7 days) • Optimal sink: dissolved oxygen < 0.47 mg O 2 L−1 and N loading rate > 150 mg N L−1 d−1. Implementation of mainstream partial nitritation/anammox (PN/A) can lead to more sustainable and cost-effective sewage treatment. For mainstream PN/A reactor, an integrated fixed-film activated sludge (IFAS) was operated (26 °C). The effects of floccular aerobic sludge retention time (AerSRT floc), a novel aeration strategy, and N-loading rate were tested to optimize the operational strategy. The best performance was observed with a low, but sufficient AerSRT floc (~7d) and continuous aeration with two alternating dissolved oxygen setpoints: 10 min at 0.07–0.13 mg O 2 L−1 and 5 min at 0.27–0.43 mg O 2 L−1. Nitrogen removal rates were 122 ± 23 mg N L−1 d−1, and removal efficiencies 73 ± 13%. These conditions enabled flocs to act as nitrite sources while the carriers were nitrite sinks, with low abundance of nitrite oxidizing bacteria. The operational strategies in the source-sink framework can serve as a guideline for successful operation of mainstream PN/A reactors. [ABSTRACT FROM AUTHOR]

This paper addresses the incorporation of transmission switching in the contingency-constrained unit commitment problem within the context of co-optimized electricity markets for energy and reserves. The proposed generation scheduling model differs from existing formulations due to the joint consideration of four major complicating factors. First, transmission switching actions are considered both in the pre- and post-contingency states, thereby requiring binary post-contingency variables. Secondly, generation scheduling and transmission switching actions are co-optimized. In addition, the time-coupled operation of generating units is precisely characterized. Finally, practical features of modern power systems, such as uncertain nodal net injections and the operation of energy storage, are also considered. The proposed model is cast as a challenging mixed-integer program for which the off-the-shelf software customarily used for simpler models may lead to intractability even for moderately-sized instances. In order to circumvent this computational issue, this paper presents an enhanced and novel application of an exact nested column-and-constraint generation algorithm featuring the inclusion of valid constraints to improve the overall computational performance. Numerical simulations based on the IEEE 118- and 300-bus systems demonstrate the effective performance of the proposed approach as well as its economic and operational advantages over existing models disregarding post-contingency transmission switching. [ABSTRACT FROM AUTHOR]

This paper addresses the multistage or dynamic investment decision-making problem arising in a distribution system within a transactive energy environment. Under such a framework, three agents are involved, namely the distribution company (DISCO), the owner of distributed generation (DG) units (DGENCO), and the independent distribution system operator (IDSO). Here, we propose characterizing this planning problem as an instance of trilevel programming. In the upper level, the DISCO identifies the optimal investment plan in network assets and the best potential locations for DG in order to supply the customer demand at maximum profit. In the middle level, the DGENCO determines the best location, sizing, and timing for DG installation so that the corresponding profit is maximized. Finally, in the lower level, the IDSO is responsible for the optimal operation of the expanded distribution system. The resulting mixed-integer trilevel program is solved by a novel approach relying on Benders decomposition. The performance of the proposed approach has been illustrated with a case study based on a 37-node test system. [ABSTRACT FROM AUTHOR]

This paper proposes a novel model for the day-ahead self-scheduling problem of a virtual power plant trading in both energy and reserve electricity markets. The virtual power plant comprises a conventional power plant, an energy storage facility, a wind power unit, and a flexible demand. This multi-component system participates in energy and reserve electricity markets as a single entity in order to optimize the use of energy resources. As a salient feature, the proposed model considers the uncertainty associated with the virtual power plant being called upon by the system operator to deploy reserves. In addition, uncertainty in available wind power generation and requests for reserve deployment is modeled using confidence bounds and intervals, respectively, while uncertainty in market prices is modeled using scenarios. The resulting model is thus cast as a stochastic adaptive robust optimization problem, which is solved using a column-and-constraint generation algorithm. Results from a case study illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper investigates the multistage expansion planning problem of a distribution network considering reliability. Thus, the best alternative, location, and installation time for the candidate assets are identified while jointly accounting for economic and reliability aspects. As a major salient feature, the conventional simulation-based reliability assessment is equivalently implemented through algebraic expressions whereby the effect of the network topology is explicitly represented by decision variables of the optimization process. For expository purposes, the focus is placed on the expected energy not supplied, which is a widely-used metric for reliability assessment. The resulting optimization problem is cast as an instance of mixed-integer linear programming. Hence, unlike existing heuristic and metaheuristic solution techniques for reliability-constrained distribution system planning, the proposed approach is finitely convergent to the optimal solution and can be readily implemented using commercially available software. Simulation results show the effective performance of the proposed methodology. [ABSTRACT FROM PUBLISHER]

Two-stage robust optimization has emerged as a relevant approach to deal with uncertain demand and generation capacity in the transmission network expansion planning problem. Unfortunately, the solution of practical large-scale instances remains a challenge. In order to address this issue, this paper presents an alternative column-and-constraint generation algorithm wherein the max–min problem associated with the second stage is solved by a block coordinate descent method. As a major salient feature, the proposed approach does not rely on the transformation of the second-stage problem to a single-level equivalent. As a consequence, bilinear terms involving dual variables or Lagrange multipliers do not arise, thereby precluding the use of computationally expensive big-M-based linearization schemes. Thus, not only is the computational effort reduced, but also the typically overlooked case-dependent, nontrivial, and time-consuming tuning of bounding parameters for dual variables or Lagrange multipliers is avoided. The practical applicability of the proposed methodology is confirmed by numerical testing on several benchmarks including a case based on the Polish 2383-bus system, which is well beyond the capability of the robust methods available in the literature. [ABSTRACT FROM AUTHOR]

This paper addresses the multistage expansion planning problem of a distribution system where investments in the distribution network and in distributed generation are jointly considered. Network expansion comprises several alternatives for feeders and transformers. Analogously, the installation of distributed generation takes into account several alternatives for conventional and wind generators. Unlike what is customarily done, a set of candidate nodes for generator installation is considered. Thus, the optimal expansion plan identifies the best alternative, location, and installation time for the candidate assets. The model is driven by the minimization of the net present value of the total cost including the costs related to investment, maintenance, production, losses, and unserved energy. The costs of energy losses are modeled by a piecewise linear approximation. As another distinctive feature, radiality conditions are specifically tailored to accommodate the presence of distributed generation in order to avoid the isolation of distributed generators and the issues associated with transfer nodes. The resulting optimization problem is a mixed-integer linear program for which finite convergence to optimality is guaranteed and efficient off-the-shelf software is available. Numerical results illustrate the effective performance of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for the transmission network expansion planning under generalized joint generation and transmission n-K security criteria. The proposed methodology identifies the optimal expansion plan while modeling the power system operation under both normal and contingency states. An adjustable robust optimization approach is presented to circumvent the tractability issues associated with conventional contingency-constrained methods relying on explicitly modeling the whole contingency set. The adjustable robust model is formulated as a trilevel programming problem. The upper-level problem aims at minimizing the investment, operation, and system power imbalance costs. The middle-level problem identifies, for a given expansion plan, the contingency state leading to maximum power imbalance if any. Finally, the lower-level problem models the operator's best reaction for a given contingency and investment plan by minimizing the system power imbalance. The resulting trilevel program is solved by a primal-dual algorithm based on Benders decomposition combined with a column-and-constraint generation procedure. The proposed approach is finitely convergent to the optimal solution and provides a measure of the distance to the optimum. Simulation results show the superiority of the proposed methodology over conventional contingency-constrained models. [ABSTRACT FROM PUBLISHER]

This paper presents a new approach for energy and reserve scheduling in electricity markets subject to transmission flow limits. Security is imposed by guaranteeing power balance under each contingency state including both generation and transmission assets. The model is general enough to embody a joint generation and transmission n-K security criterion and its variants. An adjustable robust optimization approach is presented to circumvent the tractability issues associated with conventional contingency-constrained methods relying on explicitly modeling the whole contingency set. The adjustable robust model is formulated as a trilevel programming problem. The upper-level problem aims at minimizing total costs of energy and reserves while ensuring that the system is able to withstand each contingency. The middle-level problem identifies, for a given pre-contingency schedule, the contingency state leading to maximum power imbalance if any. Finally, the lower-level problem models the operator's best reaction for a given contingency by minimizing the system power imbalance. The proposed trilevel problem is solved by a Benders decomposition approach. For computation purposes, a tighter formulation for the master problem is presented. Our approach is finitely convergent to the optimal solution and provides a measure of the distance to the optimum. Simulation results show the superiority of the proposed methodology over conventional contingency-constrained models. [ABSTRACT FROM PUBLISHER]

Waste gas fermentation powered by renewable H 2 is reaching kiloton scale. The presence of sulfide, inherent to many waste gases, can cause inhibition, requiring additional gas treatment. In this work, acetogenesis and methanogenesis inhibition by sulfide were studied in a 10-L mixed-culture fermenter, supplied with CO 2 and connected with a water electrolysis unit for electricity-powered H 2 supply. Three cycles of inhibition (1.3 mM total dissolved sulfide (TDS)) and recovery were applied, then the fermenter was operated at 0.5 mM TDS for 35 days. During operation at 0.5 mM TDS the acetate production rate reached 7.1 ± 1.5 mmol C L−1 d−1. Furthermore, 43.7 ± 15.6% of the electrons, provided as H 2 , were distributed to acetate and 7.7 ± 4.1% to butyrate, the second most abundant fermentation product. Selectivity of sulfide as inhibitor was demonstrated by a 7 days lag-phase of methanogenesis recovery, compared to 48 h for acetogenesis and by the less than 1% electrons distribution to CH 4 , under 0.5 mM TDS. The microbial community was dominated by Eubacterium , Proteiniphilum and an unclassified member of the Eggerthellaceae family. The taxonomic diversity of the community decreased and conversely the phenotypic diversity increased, during operation. This work illustrated the scale-up potential of waste gas fermentations, by elucidating the effect of sulfide as a common gas impurity, and by demonstrating continuous, potentially renewable supply of electrons. [Display omitted] • A 10-L scale CO 2 fermentation was tested using reducing power obtained by electrolysis. • Sulfide at 1.3 mM inhibits acetogenesis but the effect is reversible. • 1.3 mM TDS irreversibly suppressed methanogenesis in H 2 /CO 2 fermentation. • Under sulfide stress acetogenesis shifts towards higher butyrate production. [ABSTRACT FROM AUTHOR]

Reliability is an essential factor in distribution networkt expansion planning. However, standard distribution reliability assessment techniques rely on quantifying the impact of a pre-specified set of events on service continuity through the simulation of component outages, one at a time. Due to such a simulation-based nature, the incorporation of reliability into distribution network expansion planning has customarily required the application of heuristic and metaheuristic approaches. Recently, alternative mixed-integer linear programming (MILP) models have been proposed for distribution network expansion planning considering reliability. Nonetheless, such models suffer from either low computational efficiency or over-simplification. To overcome these shortcomings, this paper proposes an enhanced MILP model for multistage reliability-constrained distribution network expansion planning. Leveraging an efficient, yet accurate reliability evaluation model, proposing a customized technique for effectively imposing radial operation, as well as utilizing pragmatic measures to model reliability-related costs are the salient features of this work. In this respect, practical reliability-related costs are considered based on reliability incentive schemes and the revenue lost due to undelivered energy during customer outages. The proposed planning approach is tested on four networks with 24, 54, 86, and 138 nodes to illustrate its efficiency and applicability. [ABSTRACT FROM AUTHOR]

The calculation of standard reliability indices is critical for assessing quality service and guiding the planning and operation of distribution systems. Traditionally, such calculation has relied on the application of analytical methods based on simulation techniques, which preclude the use of exact methodologies when reliability assessment is incorporated into operation and planning models. This shortcoming has been recently addressed by the development of two optimization-based approaches for the analytical reliability assessment of distribution systems. Unfortunately, both methods are either incomplete or computationally expensive as compared with those based on simulation. In order to overcome these issues, this paper presents a novel and efficient algebraic approach to calculate the standard network-dependent reliability indices of distribution systems. As a distinctive feature over previous non-simulation-based methods, no optimization process is involved. Thus, the proposed approach relies on the solution of a set of linear equations for which effective algorithms are available. Several benchmarks including a real-life 1080-node system have been used to demonstrate the computational superiority of the proposed method. The successful numerical experience supports the suitability of the proposed algebraic model for reliability-constrained distribution system operation and planning. [ABSTRACT FROM AUTHOR]

This paper addresses the transmission network expansion planning problem under uncertain demand and generation capacity. A two-stage adaptive robust optimization framework is adopted whereby the worst-case operating cost is accounted for under a given user-defined uncertainty set. This paper differs from previously reported robust solutions in two respects. First, the typically disregarded correlation of uncertainty sources is explicitly considered through an ellipsoidal uncertainty set relying on their variance-covariance matrix. In addition, we describe the analogy between the corresponding second-stage problem and a certain class of mathematical programs arising in structural reliability. This analogy gives rise to a relevant probabilistic interpretation of the second stage, thereby revealing an undisclosed feature of the worst-case setting characterizing robust optimization with ellipsoidal uncertainty sets. More importantly, a novel nested decomposition approach based on results from structural reliability is devised to solve the proposed robust counterpart, which is cast as an instance of mixed-integer trilevel programming. Numerical results from several case studies demonstrate that the effect of correlated uncertainty can be captured by the proposed robust approach. [ABSTRACT FROM AUTHOR]

Presents a multiperiod electricity auction market tool that take into account transmission congestion and losses, along with several other factors. Formulation of the pool auction; Linearization of the network constraint; Application of the proposed approach to the Institute of Electrical and Electronic Engineers' Reliability Test System.