Coordinated Operation of Gas and Electricity Systems for Flexibility Study
The increased interdependencies between electricity and gas systems driven by gas-fired power plants and gas electricity-driven compressors necessitates detailed investigation of such interdependencies, especially in the context of an increased share of renewable energy sources. In this paper, the value of an integrated approach for operating gas and electricity systems is assessed. An outer approximation with equality relaxation (OA/ER) method is used to deal with the optimization class of the mixed-integer non-linear problem of the integrated operation of gas and electricity systems. This method significantly improved the efficiency of the solution algorithm and achieved a nearly 40% reduction in computation time compared to successive linear programming. The value of flexibility technologies, including flexible gas compressors, demand-side response, battery storage, and power-to-gas, is quantified in the operation of integrated gas and electricity systems in GB 2030 energy scenarios for different renewable generation penetration levels. The modeling demonstrates that the flexibility options will enable significant cost savings in the annual operational costs of gas and electricity systems (up to 21%). On the other hand, the analysis carried out indicates that deployment of flexibility technologies appropriately supports the interaction between gas and electricity systems.
1. Introduction
The share
of variable Renewable Energy Sources (RES) in the power generation mix is
increasing significantly in Great Britain (GB) to meet de-carbonization targets
(National Grid Plc, 2016).
Gas plants are expected to contribute to the management of the variability of
renewable energy generation, which consequently will increase the interaction
between gas and electricity systems and increase challenges associated with the
management of gas storage and linepack in the gas transmission system.
Therefore, operating the gas and electricity systems as an integrated energy
system is increasingly important.
Battery
storage, Demand-Side Response (DSR), power-to-gas (P2G), and flexible
compressors can enhance the system flexibility needed to support more
cost-effective balancing of electricity demand and supply. Furthermore, these
options can participate in the provision of various ancillary services,
including reserve and frequency regulation (Qadrdan et al., 2017b).
Battery storage facilitates the integration of wind into the grid through
managing variation of the peak plants, such as gas-fired plants. The employment
of DSR helps to deal with the variability of RES better, as energy consumption
can be shifted, which can act as a virtual power plant (Ameli et al., 2017a,b). Furthermore,
P2G technologies would make use of a surplus of renewable electricity by
producing hydrogen via electrolyzers that would be injected into the gas system
or stored in hydrogen storage facilities. Afterward, the hydrogen can be
transported to the demand centers or provided to Combined Cycle Gas Turbines
(CCGTs) to produce free-carbon electricity. In the gas system, flexible gas
compressors improve gas delivery to the demand centers through changing the gas
flow direction. Several studies, such as Troy et al. (2012) and Pudjianto et al. (2014),
have evaluated the role of flexibility options in addressing the electricity
balancing challenges caused by RES.
From whole energy system perspectives, by taking flexibilities into account, the interaction of gas and electricity systems was studied in Correa-Posada and Sanchez-Martin (2015), He et al. (2017), Zlotnik et al. (2017), Qadrdan et al. (2017a), Ameli et al. (2017c,d), and Wu et al. (2019). Zlotnik et al. (2017) developed coordinated modeling of interdependent gas and electricity systems for day-ahead scheduling of power dispatch and gas compressor operation. The efficiency of the model was validated by improvement in system operation and cost reduction. In Ameli et al. (2017d), the role of multi-directional compressors as one of the options in making the gas system more flexible was investigated in different operation methodologies of gas and electricity systems. It was demonstrated that increased flexibility in the gas system is beneficial for the whole energy system. In Sheikhi et al. (2015), an integrated demand-side response framework as a part of a smart energy hub was proposed. In this framework, the customer can modify the use of gas or electricity based on the gas and electricity prices. It was shown that this approach offers benefits for both customers and utilities in terms of costs and profits. In Yang et al. (2019), the coordination of different P2G conversions, including electrolysis and Steam Methane Reforming (SMR), and gas-fired plants in an integrated operation of gas and electricity networks was proposed. It was shown how this combined model can improve energy efficiency and reduce carbon emissions compared to the power-to-hydrogen-to-methane-to-pipeline approach. From a modeling point of view, it was not mentioned in detail how this optimization problem may be solved. In He et al. (2017), coordinated scheduling of gas and electricity systems considering P2G was investigated. Furthermore, another study (Akhtari and Baneshi, 2019) showed how the excess electricity generated by renewables can be used in the electrolysis process to produce hydrogen.
The proposed method was tested in five different cities, and a decrease in carbon emissions was reported. In Wu et al. (2019), a hybrid multi-objective optimization approach was developed for the operation of integrated energy systems considering gas and electricity. In this approach, the price of electricity and cooling demands are considered. The results indicated fair treatment for all the players in the integrated energy system. In Zeng et al. (2016), a bi-directional energy flow between gas and electricity systems was proposed to realize high penetration of renewables and an increase in system flexibility. The effectiveness of the proposed method (i.e., solved by the Newton-Raphson method) was analyzed on an IEEE-9 test system and a 7-node gas system. In Correa-Posada and Sanchez-Martin (2015), a coupled model of natural gas and power systems aimed at providing energy adequacy was presented. Non-linear equations and constraints were linearized to solve a Mixed-Integer Linear Programming (MILP) problem. A weak point of this study was that linearizing the non-linearities piecewise causes a significant increase in the probability of data loss. In Gil et al. (2016), two coupling methodologies for gas and electricity markets in a European regulatory framework were presented. The first methodology was based on maximizing the profit of the electricity market, and the second approach was based on minimizing the operational cost of the natural gas system. It was demonstrated that if the modeling is accurate, the difference between these two methodologies may be negligible. In addition, in Zlotnik et al. (2017), different coordinated scheduling scenarios of natural gas and power systems were presented. The Unit Commitment (UC) problem of the generation units was not considered. This was done in order to reduce the model complexity by preventing binary variables in the optimization procedure, which may lead to inaccuracy. The authors of Deane et al. (2017) built and applied an integrated electricity and gas model for the European Union system. In this research, gas supply interruption scenarios were derived to examine the impacts on power system operation. As an example, it was shown that interruption of the Russian gas supply to the EU enhanced the average gas price by 28% and the electricity price by 12%. In Sardou et al. (2018), the role of microgrid aggregators in a coordinated operation strategy for gas and electricity systems was investigated. In Zhang et al. (2016), the role of demand response in providing energy balance was considered. A coordinated MILP strategy for natural gas and power systems was proposed. In this strategy, the power system was optimized, and then the natural gas constraints were checked for the feasibility of the solution. It was shown that this model increased the social welfare of the scenarios. However, through linearizing the gas flow equation piecewise, the complexity of the model is reduced, and accuracy may be lost. In the literature, different methods have been applied to linearize the general gas flow and propose a MILP formulation for the operation of a gas network (Correa-Posadaa and Sanchez-Martin, 2014; He et al., 2017; HU et al., 2017; Sirvent et al., 2017). Although piecewise linearization affects the time required to solve the problem considerably, the accuracy of each method (i.e., ability to find the optimal solution) significantly relies on the generating segments. On the other hand, some methods are not scalable and can only be used for a problem of a predetermined size (Correa-Posadaa and Sanchez-Martin, 2014).The coupling of the binary variables representing the On/Off states of generating units and non-linear equations of gas flow in pipes and compressor power consumption makes the optimization of the integrated operation of gas and electricity systems a Mixed-Integer Non-Linear Programming (MINLP) problem, which is complex and challenging to solve from the computational perspective (Floudas, 1995). In order to deal with the aforementioned complexity in solving the MINLP problem, several algorithms, such as Generalized Benders Decomposition (GBD), Outer Approximation (OA), Outer Approximation with Equality Relaxation (OA/ER), and generalized cross decomposition, have been developed (Floudas, 1995). Deterministic methods, such as Lagrangian Relaxation (LR) (Ongsakul and Petcharaks, 2004) and Benders Decomposition (BD) (Nasri et al., 2016), and also heuristic methods, such as an evolutionary algorithm (Chung et al., 2011) have been applied to solve MINLP problems in power systems. In Shabanpour-Haghighi and Seifi (2015), a solving technique based on a modified teaching–learning method for optimal power flow taking electricity, gas, and heat into account was proposed. This method was evaluated and compared with conventional evolutionary algorithms to highlight the effectiveness of the method. In He et al. (2017), co-optimization scheduling of gas and electricity systems was proposed. A decomposition method was applied to solve the electricity system sub-problem and gas system sub-problem separately.
The OA approach, which is the fundamental technique in this study, has been implemented in a few studies for dealing with the Unit Commitment (UC) problem (Yang et al., 2017) with AC power flow (Castillo et al., 2016) as well as security-constrained UC (Dai et al., 2016). The OA/ER decomposition method solves a binary relaxed primal problem [Non-Linear Problem (NLP)] and a relaxed master problem (MILP). The OA/ER decomposition method copes with non-linear inequalities and consequently creates sequences of lower and upper bounds. In the OA/ER approach, the non-linear equalities are converted to linear inequalities based on their associated Lagrangian multipliers. It is worth mentioning that the integrated operation of gas and electricity systems is solved by Successive Linear Programming (SLP) (Default solver of Xpress FICO, 2013) and investigated from different aspects in a few papers, such as Qadrdan et al. (2017a) and Ameli et al. (2017d). The MINLP problem of integrated operation of gas and electricity is non-convex, which implies the potential existence of multiple local optima.
Hence, in this paper, in order to deal with the complexity of
the above-mentioned model, a solution algorithm is implemented based on the
OA/ER approach to model the integrated operation of gas and electricity
systems. The efficiency of this decomposition method is validated by comparing the
computational performance in terms of optimization time and objective function
with the SLP method. Furthermore, the role and value of the flexibility
options, including DSR, electricity storage, flexible gas plants, P2G, and
multi-directional compressors, in the cost-effective operation of the
integrated systems for intact and contingency configurations (i.e., gas supply
interruption) on a 2030 GB system are investigated. In this regard, to evaluate
the sensitivity of the renewable penetration level to the flexibility options,
different renewable generation and gas supply development scenarios in the
presence of different installed capacities of flexibility options are defined
to quantify the operation of the energy systems. To model the entire year, a demand
clustering method is developed to reduce the size of the optimization problem,
so that, through this method, the entire year is represented by 12 days.