Unit Dispatch Research Articles

Data-driven energy management of isolated power systems under rapidly varying operating conditions

We propose an energy management algorithm for isolated industrial power systems that integrate uncertain renewable generation and energy storage. The proposed strategy is designed to ensure sustainable and cost-effective operations by managing the energy flows in the grid, and is structured so to cope with: (1) high levels of renewable power penetration, and (2) load profiles characterized by non-smooth patterns and irregular events (i.e., events such as those occurring from connections/disconnections of large scale equipment, or from large wind speed ramps). The proposed algorithm leverages a stochastic economic model predictive control (MPC) scheme capable of dealing simultaneously with the dispatch and scheduling of the local generation units. More precisely, the scheme embeds a mixed-integer linear programming (MILP) optimal control policy formulation together with a stochastic programming approach. Moreover, the optimization problem accounts for multiple techno-economical objectives, such as minimization of operational costs, battery degradation, and non-utilized energy. We test the algorithm on a case study of an isolated offshore Oil & Gas platform producing energy onsite with conventional gas turbines and a local wind farm, while integrating a battery energy storage system. The results show that the proposed approach can issue ensemble predictions that successfully capture the potential irregular variations just by using recent past information of the associated random variable, even when no particular sudden events are anticipated in the near-future (i.e., step changes/trend reversals). In this way, the approach provides useful future information for the optimal management of the grid. This effect is numerically quantified via simulations that compare the performance of the proposed stochastic optimization approach against its deterministic MPC version in several realistic operating conditions. The empirical results suggest that the stochastic version leads to better scheduling of the conventional generators, with up to 12.86% reductions of the operating cost, 2.56% reduction in fuel consumption and emissions, and 35.29% reduction in status transitions (on/off) of the gas turbines, while keeping dumped energy and battery degradation as low as possible.

Strategic bidding in electricity markets with convexified AC market-clearing process

This paper presents a framework to solve the strategic bidding problem of participants in an electricity market cleared by employing the full AC Optimal Power Flow (ACOPF) problem formulation. Traditionally, the Independent System Operators (ISOs) leveraged DC Optimal Power Flow (DCOPF) problem formulation to settle the electricity market. The main quest of this work is to find what would be the challenges and opportunities if ISOs leveraged the full ACOPF as the Market-Clearing Problem (MCP)? This paper presents tractable mathematical programming with equilibrium constraints for the convexified AC market-clearing problem. Market participants maximize their profit via strategic bidding while considering the reactive power dispatch of generation units. The equilibrium constraints are procured by presenting the dual form of the relaxed ACOPF problem. The strategic bidding problem with an ACOPF-based MCP improves the exactness of the Location Marginal Prices (LMPs) and profit of market participants compared to the one with DCOPF. It is shown that the strategic bidding problem with DCOFP-based MCP is unable to model the limitation of reactive power support. The presented results display cases where the proposed strategic bidding method renders 52.3% more profit for the Generation Company (GENCO) than the DCOPF-based MCP model. The proposed strategic bidding framework also addresses the challenges of coupling real and reactive power dispatch of generation constraints, ramping constraints, demand response implications with curtailable and time shiftable loads, and AC line flow constraints. Therefore, the presented method will help market participants leverage the more accurate ACOPF model in the strategic bidding problem.

Solar photovoltaic dispatch margins with stochastic unbalanced demand in distribution networks

The increase in the generation capacity of the variable photovoltaic units introduces new challenges to the operation of the unbalanced three-phase distribution networks. In this paper, a two-stage optimization problem is formulated to identify the feasible dispatch margins of photovoltaic generation considering the distribution network operation constraints. The proposed problem is solved using the column-and-constraint generation approach. The distribution network constraints are formulated as second-order cone constraints. The uncertainty in the forecasted demand and maximum photovoltaic generation as well as the unbalanced operation of the distribution network is considered in the proposed approach. The dispatch margins of photovoltaic generation are quantified considering the worst-case realization of demand in the distribution network. The impacts of energy storage and the ramping limits of the dispatchable generation resources on the dispatch margins of photovoltaic generation are addressed in this network. The dispatch margins of photovoltaic generation are quantified in the modified IEEE 13-bus system. It is shown that enforcing the ramping rates for the dispatchable units will increase the lower dispatch margins of photovoltaic generation, and leveraging energy storage increases the difference between the lower and upper photovoltaic dispatch margins.

Data-driven structural modeling of electricity price dynamics

In many countries, electricity prices on day-ahead auction markets result from a market clearing designed to maximize social welfare. For each hour of the day, the market price can be represented as the intersection of a supply and demand curve. Structural market models reflect this price formation mechanism and are widely used in prospective studies guiding long-term decisions (e.g. investments and market design). However, simulating the supply curve in these models proves challenging since estimating the sell orders it comprises (i.e. offer prices and corresponding quantities) typically requires formulating numerous techno-economic hypotheses about power system assets and the behaviors of market participants. Due to imperfect competition, real market prices differ from the theoretical optimum, but modeling this difference is not straightforward. The objective of this work is to propose a model to simulate prices on day-ahead markets that account for the optimal economic dispatch of generation units, while also making use of historical day-ahead market prices. Inferring from historical data is especially important when not all information is made public (e.g. bidding strategies) or due to difficulty in accurately accounting for qualitative notions in quantitative models (e.g. market power). In this paper we propose a method for the parametrization of sell orders associated with production units. The estimation algorithm for this parametrization makes it possible to mitigate the requirement for analytic formulation of all of the above-mentioned aspects and to take advantage of the ever-increasing volume of available data on power systems (e.g. technical and market data). Parametrized orders also offer the possibility to account for various factors in a modular fashion, such as the strategic behavior of market participants. The proposed approach is validated using data related to the French day-ahead market and power system, for the period from 2015 to 2018.

An improved Newton-Raphson based linear power flow method for DC grids with dispatchable DGs and ZIP loads

PurposeThis study aims to provide a solution of the power flow calculation for the low-voltage ditrect current power grid. The direct current (DC) power grid is becoming a reliable and economic alternative to millions of residential loads. The power flow (PF) in the DC network has some similarities with the alternative current case, but there are important differences that deserve to be further concerned. Moreover, the dispatchable distributed generators (DGs) in DC network can realize the flexible voltage control based on droop-control or virtual impedance-based methods. Thus, DC PF problems are still required to further study, such as hosting all load types and different DGs.Design/methodology/approachThe DC power analysis was explored in this paper, and an improved Newton–Raphson based linear PF method has been proposed. Considering that constant impedance (CR), constant current (CI) and constant power (CP) (ZIP) loads can get close to the practical load level, ZIP load has been merged into the linear PF method. Moreover, DGs are much common and can be easily connected to the DC grid, so V nodes and the dispatchable DG units with droop control have been further taken into account in the proposed method.FindingsThe performance and advantages of the proposed method are investigated based on the results of the various test systems. The two existing linear models were used to compare with the proposed linear method. The numerical results demonstrate enough accuracy, strong robustness and high computational efficiency of the proposed linear method even in the heavily-loaded conditions and with 10 times the line resistances.Originality/valueThe conductance corresponding to each constant resistance load and the equivalent conductance for the dispatchable unit can be directly merged into the self-conductance (diagonal component) of the conductance matrix. The constant current loads and the injection powers from dispatchable DG units can be treated as the current sources in the proposed method. All of those make the PF model much clear and simple. It is capable of offering enough accuracy level, and it is suitable for applications in DC networks that require a large number of repeated PF calculations to optimize the energy flows under different scenarios.

Frequency Stability Constrained Microgrid Scheduling Considering Seamless Islanding

The frequency stability of microgrids is of paramount importance due to their low inertia and high share of renewable energy sources. In this paper, a frequency stability-constrained microgrid scheduling (FSC-MS) model is developed based on a time-dependent discretized frequency response model considering seamless unintentional islanding events. The optimal dispatch of controllable units, as well as optimal upward and downward primary reserves, are determined with a minimum operating cost while ensuring frequency stability criteria are maintained in their safe ranges following an unintentional islanding event, such as rate of change of frequency (RoCoF) and frequency nadir and overshoot. The limitation of generators with respect to primary frequency response is formulated in the form of primary reserve capacity and primary ramp rate limits. A cost-based model is presented to describe the available primary reserve of synchronous generators. The proposed FSC-MS model is formulated as a mixed-integer linear programming (MILP) problem using Benders decomposition. Simulation results on a medium voltage microgrid test system demonstrate the effectiveness of the proposed model to meet the frequency stability constraints while minimizing the total operating cost.

Blockchain based optimal decision making of dispatchable units in smart grids considering the high uncertainty effects

Using blockchain technology (BCT) in securing data has received a lot of attention these days. This technology is used not only as a new way with the aim of enhancing the cyber and physical security of power systems, but also has been applied to enhance the exchanged information between units. When Fuel Cell (FC), photovoltaic cells (PV) and wind turbines (WT) are used as sources of distributed generation (DG), there will be a situation called Islanding Micro-Grid (MG), so that this situation has the ability to control voltage, frequency and current of the system with no dependence on the utility. Hence, for overcoming load variations and disturbances, designing and implementing a robust controller is vital. Accordingly, a robust General Type-2 Fuzzy sliding mode controller (GT2FSMC) on the basis of gray wolf optimization algorithm is presented in the following study with the purpose of enhancing the power sharing amongst the distributed generations operating in the off-grid MG. The offered controller, in addition to warrant the stability and robust versus uncertainties of the lumped generated via extrinsic disturbances and un-modelled dynamics, has the ability to significantly decrease the control chattering inherent in primary sliding mode control. In the following paper, it is assumed that there are two distributed generations, each with specific duties. One has the duty of regulating the frequency and voltage through a reference signal, and another is responsible for working in load current control mode with the aim of sharing load correctly among each of them. For stopping malicious attacks, an assured policy framework according to the blockchain (BC) has been created to enhance the security of interchanged data among production units. Plus, two low-voltage parallel DG units exist in the MG. With the aim of evaluating the mentioned way, the simulation results have been done via MATLAB environment. According to the outcomes, it can be concluded that this mentioned controller has the ability of providing lower steady state error and Total Harmonic Distortion (THD) with faster response in comparison to the further controllers that generally are used. The simulation results show that the DG unit operations within the islanding MG have the ability of coordinating successfully under that mentioned control system with the aim of confirming stable operation of the throughout MG.

Angažovanje generatora i potrebe balansnih rezervi u prisustvu intermitentnih OIE: Studija slučaja elektroenergetskog sistema Srbije

Intermittent electricity generation of the various renewable energy sources sets additional burden on generation unit dispatching and reserve planning in day-ahead electricity market. The conventional unit commitment models which include rump-up and rump-down limits are usually presented with stepwise mathematical functions without clear distinction between power and energy. This paper presents a case study of generation unit dispatching planning approach for the case of pricewise-linear functions from one time interval to another, taking into account actual ramping trajectories thus providing more efficient reserve scheduling particularly during generators’ start-up and shut-down processes. For the representation of reserve schedules, the 10-min and 30-min ramp capabilities of the generation units are assumed where they are obliged to provide secondary, tertiary and offline-tertiary reserves depending on their technical capabilities. The Serbian power system case study represents a 24-hour unit commitment planning and up/down secondary and tertiary reserve requirements during a scarcity event when reserve flexibility of neighbouring power systems cannot be utilised. Formulation of the optimisation mathematical model is performed via GAMS, the General Algebraic Modelling System, and CPLEX optimiser is used for problem optimisation purposes.

Optimal IoT-based decision-making of smart grid dispatchable generation units using blockchain technology considering high uncertainty of system

This article proposes a blockchain-based architecture for the optimal scheduling of the dispatchable units in the smart grids considering the high penetration of renewable energy sources. The proposed method makes use of blockchain technology to secure the data exchange among the agents in the smart grid and avoid unauthorised access to the real data of the system. Considering the flexibility which is provided by the optimal switching in the smart grid, the reconfiguration problem is also considered in the model using some remote control switches. A stochastic framework based on the unscented transform is proposed to not only capture the independent randomness of the renewables but also to handle the correlated uncertainties among them. The problem formulation is constructed as a multi-objective optimization problem optimizing the total operating cost and the reliability indices. Due to the nonlinear and complex nature of the problem, a new optimization method based on the honey bee mating (HBM) algorithm is proposed to solve the problem without any simplification or linearization. Moreover, a correction approach based on local search is proposed to increase the algorithm capability. The appropriate performance of the proposed model is examined on a test smart grid. The data for the optimal decision is based on the internet of things (IoT) data.

Robust Flexible Unit Commitment in Network-Constrained Multicarrier Energy Systems

The coordinated operation of different energy systems, such as electrical, gas, and heating, can improve the efficiency of the whole energy system and facilitate the larger penetration of renewable energy resources in the electricity generation portfolio. However, appropriate models considering various technical constraints of the energy carriers (e.g., gas system pressure limit and heat losses in the district heating networks) are needed to effectively assess the true impact of integrated energy system (IES) operation on the overall system’s performance. This article proposes a flexible unit commitment (UC) problem for coordinated operation of electricity, natural gas, and district heating networks, called multicarrier network-constrained unit commitment (MNUC), to minimize the operation cost of the IES. Besides, an integrated demand response (IDR) program is considered as a promising solution to improve consumers’ electricity, gas, and heat consumption patterns and to increase the power dispatch of combined heat and power units. Multienergy storage systems are also included in the proposed model to decrease the impact of multienergy network constraints on the overall system’s performance. To model the uncertainties involved in the operation of the three networks, a combined robust/stochastic approach is preferred in the MNUC problem considering multicarrier energy storage systems and the IDR program. Numerical results show that the whole operation cost of the IES has decreased by 2.58% considering the IDR program and multienergy storage systems.

A two-stage hybrid robust-stochastic day-ahead scheduling of transactive microgrids considering the possibility of main grid disconnection

The interconnected transactive MGs (ITMGs) concept is an efficient way for economical operation of MGs. For power systems under the danger of stochastic disasters, a priority should be given to the resiliency enhancement of these ITMGs to have the highest restoration in the times of disconnection from the main grid. This paper introduces a two-stage hybrid stochastic programming/robust optimization (SP/RO) day-ahead scheduling of ITMGs that takes into account the possibility of disconnection from the main grid for several hours. The proposed resiliency-oriented scheduling in this paper is a two-stage mixed-integer linear optimization problem and aims to reduce the operation cost of the whole ITMG. Furthermore, the excess power can be traded among MGs apart from the available power of renewable resources, dispatched power of storage and dispatchable units, and demand response programs. Thus, this scheduling needs to consider the optimal hourly transactive energy among the MGs in both normal and grid disconnected modes of the ITMG. The load curtailment and demand response programs are the last options for maintaining the balance in the conventional structure of MGs that this paper utilizes for ITMG. Obtained results showed that load curtailment of the ITMG is limited by the implementation of transactive energy between MGs. Furthermore, the reduction in renewable and dispatchable generation capacity and their impact on transactive energy and demand response programs during resiliency is discussed. Also, to enhance the conservativeness and to consider the worst cases in this scheduling, the uncertainties in the start time of the islanding and its duration are considered in the problem.

The indirect impact of COVID-19 pandemic on the utilization of the emergency medical services

BackgroundFear of contracting COVID-19 and the large-scale preventing measures may have influenced health service utilization. The aim of the study was to assess the indirect impact of COVID-19 pandemic on the utilization of the emergency medical services (EMS) of Tuscany Region (Italy).MethodsThe study was carried out on administrative data from the public healthcare system of Tuscany (38 general hospitals and 4 university teaching hospitals). Variations in the utilization of out-of-hospital (i.e. calls for emergency medical assistance and dispatch of mobile medical care units) and in-hospitals (emergency department(ED) visits and hospitalizations from the ED) EMS during the first pandemic wave were analyzed (February - July 2020) in relation with corresponding periods of the previous three years. Furthermore, in order to understand changes in illness severity of patients using the EMS, standardized ratios for hospitalizations requiring ICU and in-hospital mortality were analyzed.ResultsSignificant decreases in the expected volumes of utilizations were observed in each of the considered EMS starting from the week in which the first case of COVID-19 was confirmed in Italy till the last week of the first wave. In particular, during the peak period of COVID-19 pandemic wave a reduction of 73,041 (-67%, 95%CI -69%; -66%) in ED admissions and of 682 (-38% 95%CI -55%; -43%) in hospitalization from ED were observed. Significant decreases in hospitalizations for life threatening medical conditions were observed. No significant differences were found between the observed and the expected in-hospital mortality and hospitalizations requiring ICU during the COVID-19 epidemic peak period (SR 1.1 95%CI 0.72-1.90; SR 1.0 95%CI 0.83-1.23).ConclusionsA large and generalized decrease in EMS utilization was observed during the first wave of COVID-19 pandemic. Furthermore, findings suggest that seriously ill patients avoid seeking care as less seriously ill patients did.Key messages The falling in the utilization of emergency medical services during pandemic could portend substantial harm to public health, and not simply the absence of real health need.The preparedness of the healthcare delivery system should not focus solely on confronting the pandemic disease, but also on ensuring access to high quality emergency care for other diseases.

Dispatching System of New Energy Connected to Grid under Smart Grid

The introduction of smart loads in the dispatching of the distribution network helps to make full use of new energy generation and reduce the dispatch of standby units. Summarize the characteristics of distributed energy generation and load, introduce intelligent load and its application in distribution network dispatching. In view of the distributed characteristics of new energy, neural network is used to predict in the distribution network dispatching with distributed energy access. The coordination of the multi-agent system can achieve good distribution network scheduling effects, and a single agent can also use its own autonomy and learning experience to participate in the distribution network scheduling.

Optimal Dispatch of Renewable and Virtual Power Plants in Smart Grid Environment through Bilateral Transactions

In a competitive power market, assessment of optimal bilateral transactions is very important for achieving more financial benefits without any stability, security and reliability issues. Due to the open access transmission network and the wide deployment of renewable energy sources (RES) and virtual power plants (VPP), dispatch of VPP and RES units through bilateral transactions become complex. This paper presents an efficient approach for the determination of optimal and feasible bilateral transactions among the power traders in a smart grid based on proposed factors, i.e., active participation factor (APF), maximum loading point (MLP) and load distribution factor (LDF). The objective of the proposed tri-level dispatch model is to minimize the cost of generation and transmission cost of transactions by using a mixed integer nonlinear programming (MINLP) technique. The proposed market dispatch model efficiently gets rid of the congestion in the transmission network and enhances the power transfer capability of the network, i.e., transaction dispatch capability of the network. The performance of the proposed tri-level model has been validated on the modified IEEE-14 bus system and modified IEEE-30 bus system.

Economic Dispatch of CHP Units through District Heating Network’s Demand-Side Management

Optimisation of heat and electrical load distribution, where the objective function is the maximum efficiency of the CHP unit for a given load range, can be done considering the limitations of electrical power and the heat load. Simulating a real CHP unit with a district heating network shows that demand-side management can improve the overall economic efficiency of the CHP plant and increase the unit’s operating range in the electricity spot market. Economic dispatch makes it possible to determine a reasonable additional increase in the electric power of the CHP unit, and to optimise the supply temperature and mass flow of the district heating network. The results obtained and the analysis performed indicate that the proposed methodology provides logical results and can be used to calculate the efficiency indicators of the cogeneration of electrical and thermal energy. The problem of optimising the operating mode of the CHP unit was solved, which allows us to determine the optimal additional increase in the unit’s electrical load at a given heat load of consumers, which on average increases the CHP unit’s efficiency up to an additional 1.5%.

Multi-microgrid energy management through tertiary-level control: Structure and case study

The widespread emergence of renewable energy resources has made microgrids neighbors. These microgrids share their power, and multi-microgrids are formed, which is more stable and flexible. However, the main problem of these smart grids is their complex control. To this end, tertiary-level control can play a significant role. This paper sought to cite the existing control models and strategic plans for microgrid operation, review the literature and provide a comprehensive picture of microgrid and multi-microgrids energy management at the tertiary-level control. Then, by introducing the component fatigue life as a new factor, an optimization method for energy management of multi-microgrids at the tertiary level is developed. For solving the problem, MATLAB and DigSilent software are used simultaneously. Different connection models and secondary level controller settings are determined using the binary grey wolf optimization in MATLAB. This data is provided to the DigSilent to carry out the optimal dispatch of generation units. The iterations are repeated until the desired operating point is reached. Several case studies are implemented to evaluate the performance of the proposed method. The results show that operating costs can be reduced up to 62.91% under the proposed method compared with the island mode.

Intra‐day Dynamic Optimal Dispatch for Power System Based on Deep Q‐Learning

AbstractSource‐load stochasticity affects the safety, stability and economical operation of power grid, and makes it difficult to design optimal dispatch. Currently, due to the low accuracy of day‐ahead forecast for wind power and load, dynamic optimal dispatch based on ultra‐short‐term wind power prediction is an effective way to reduce the power deviation in power system and to relieve the operating pressure on automatic generation control (AGC) unit. In this paper, by comprehensively considering the source‐load stochasticity, the dispatch properties of unit, and the constraints of AGC regulating range, we establish a dynamic optimal dispatch model to minimize the running cost of the underlying power system, including the coal consumption cost of thermal unit, and the output adjustment cost of both the dispatchable unit and the AGC unit. Since each dynamic dispatch order has an impact, direct or indirect, on the subsequent system operation, we formulate the dynamic optimal dispatch problem as a Markov decision process (MDP) problem. In this framework, a deep Q‐learning method is provided to optimize the dispatch strategy. Finally, the feasibility and the effectiveness of the proposed method is validated by means of numerical experiments on IEEE 30‐node system. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

A vision of flexible dispatchable hybrid solar‐wind‐energy storage power plant

Dispatchable renewable generation is essential for 100% renewables power system. It is in the interest of both power system operators and customers since it reduces the need for flexibility and reserve. To this end, battery energy storage systems (BESS) are proposed for integration in the renewable power plant. This paper presents the optimal dispatch unit for a dispatchable hybrid solar-wind power plant with BESS framework. It achieves optimal dispatchable renewable generation (from dispatchable hybrid renewable (solar-wind) power plant with BESS, DHRB, operator perspective), subject to operational limits, by exploiting the synergy of wind and solar energy and combining it with storage capability of BESS using two different operation strategies, maximisation of revenues and maximisation of renewables harvesting. A continuous BESS degradation model is incorporated in the proposed rolling-algorithm-based-optimal dispatch unit to improve the accuracy of results. The applicability of the proposed methodology and the performance of the operation strategies are demonstrated using a case study and the operation strategies are compared. Further, the effect of BESS size on its degradation and dispatchable power in a hybrid solar-wind power plant with BESS are investigated through BESS lifetime. An indicative economic analysis is carried out to provide ground for the extra investment on and sizing of BESS for a dispatchable hybrid renewable power plant.

Development and Validation of a Dynamic Simulation Model for an Integrated Solar Combined Cycle Power Plant

The combined cycle power plants are the most recognized thermal power plants for their high efficiency, fast start-up capability, and relatively low environmental impact. Moreover, their flexible unit dispatch supports the share of renewable energy, which contributes to carbon mitigation. The operational flexibility of Integrated Solar Combined Cycle (ISCC) power plants is a crucial factor for reliable grid stability. To evaluate the limitations and capabilities of ISCC power plants and their control structures, dynamic simulation is a feasible method. In this study, a sophisticated dynamic process model of the ISCC power plant in Kuraymat, Egypt, has been developed using APROS software. The model describes the plant with a high level of detail including the solar field, the heat recovery steam generator, and the control structures. The model was implemented structurally identical to the reference plant and tuned using the operational design data. Actual measurements were used as the basis for the initialization and validation of the dynamic simulation environment. Dynamic analysis of four different days was performed, then the simulation results were presented and compared with actual measurements. The comparison showed that the course of the actual measurements could be predicted with high accuracy. The solar field influences and the system’s overall power curves are reliably simulated. Consequently, the validated model can simulate the dynamic behavior of the ISCC power plant with a high degree of accuracy, and can be considered in future planning decisions.

Coordination of power and natural gas markets via financial instruments

Current electricity and natural gas markets operate with deterministic description of uncertain supply, and in a temporally and sectorally decoupled way. This practice in energy systems is being challenged by the increasing integration of stochastic renewable energy sources. There is a growing need for exchanging operational flexibility among energy sectors, which requires to improve the sectoral coordination between electricity and natural gas markets. In addition, the dispatch of flexible units in both sectors needs to be made in a more uncertainty-aware manner, requiring to strengthen the temporal coordination between day-ahead and real-time energy markets. We explore the use of existing financial instruments in the form of virtual bidding (VB) as a market-based solution to enhance both sectoral and temporal coordination in energy markets. It is established in the literature that VB by purely financial players is able to enhance the temporal coordination between deterministic day-ahead and real-time markets. By developing various stochastic equilibrium and optimization models, we show that VB by physical players, i.e., gas-fired power plants, at the interface of power and natural gas systems is of great potential to improve not only the temporal coordination between deterministic day-ahead and real-time markets, but also the sectoral coordination between deterministic electricity and natural gas markets. We exploit a fully stochastic co-optimization model as an ideal benchmark, and numerically illustrate the benefits of VB for increasing the overall market efficiency in terms of reduced expected operational cost of the entire energy system. We eventually show that flexible resources in both electricity and natural gas markets are dispatched more efficiently in the day-ahead stage when VB exists.