Optimal energy pricing for integrated natural gas and electric power network with considerations for techno-economic constraints

doi:10.1016/j.energy.2017.01.145

Energy

Volume 123, 15 March 2017, Pages 693-709

https://doi.org/10.1016/j.energy.2017.01.145 Get rights and content

Highlights

•
Natural gas (NG) and electricity prices are interdependent.
•
Optimal pricing of NG and electricity is simulated for an integrated network.
•
Results confirm that price of NG affects that of electricity and vice versa.
•
Electricity locational marginal price is a function of generation cost as well as network contingency status.
•
Efficiency improvement for power plant can decrease Electricity locational marginal price.

Abstract

Natural gas (NG) and electricity are delivered to end users through separate NG and power networks, however, the intrinsic characteristics and interdependency of these energy carriers affect their pricing. The objective of this study is to determine and analyze the optimal locational marginal prices for NG (GLMP) and electricity (ELMP) in an integrated natural gas and electric power network (IGPN), based on interior point algorithm for optimization subject to various techno-economic constraints. Results from scenario-based analyses of an IGPN show that (a) GLMP in each NG network (GN) node is dependent on NG cost in the nodes feeding a particular node, (b) ELMP is concurrently a function of generation cost as well as IGPN contingency status such as NG pipeline outage, power line outage, reduction in NG and electricity flows (c) the distance between gas-fired power plant (GFPP) and GN affects power generation and ELMP, and (d) efficiency improvement for GFPP can decrease total energy generation cost and ELMP.

Introduction

Utilization of energy planning based on bottom-up approach has given rise to opportunities for substitution of energy carriers [1]. However, for effective planning, the interdependency of energy carriers requires examination of price relation between them with considerations for techno-economic aspects [2]. Motivated by guarantee of rewards for suppliers, optimal pricing for energy carriers can serve as an effective mechanism for assisting consumers to make the necessary adjustments in their consumption. As such, social welfare of both suppliers and consumers are met while preserving energy resources. In the long run, it is also expected that energy generation, transmission, and distribution development and investment, as well as the quality of energy services can be improved, when optimal energy pricing is implemented. A careful examination of these issues reveals that energy pricing is a techno-economic problem, where parameters such as fuel price volatility, congestion, contingency, distance, and power generation efficiency affect the pricing scheme for different locations in energy networks, as it has recently been posed as a part of deregulated energy market [3].

Based on the realization of price relation between energy carriers, in several South America countries, where fossil fuels are regarded as the necessary feed for power generation, natural gas (NG) market joined the electricity market forming the first experiment of “integrated energy markets” [4]. Numerous studies have been conducted to examine the techno-economic aspects of NG network (GN) and electric power network (PN) in an integrated way, namely, the work by An et al., in 2003, where it is demonstrated that higher social welfare from integrated natural gas and power network (IGPN) is achievable, as compared with that from GN and PN on individual basis [2].

The key result from IGPN analyses is the pricing of the respective energy carriers as a function of production cost, generation and transmission capital cost, operating and maintenance cost, power quality supply cost, loss and congestion costs, producers rate of interest, and aggregated consumers demand pattern [3].

Next, to provide the needed background for achieving the objective of this study, the literature review on development of IGPN modeling and outcomes from previous studies are discussed.

Section snippets

Literature review

An et al. examined an integrated NG and electricity optimal power flow (GEOF) in 2003 based on primal dual interior point algorithm (IPA) for optimization and maximizing social welfare and, in a case study, two scenarios for the wellhead NG price are introduced to determine the sensitivity of generated power to such pricing approach [2]. While techno-economic issues are not addressed in that study, the findings show that the social welfare decreases if the optimal NG flow and optimal power flow

IGPN modeling and problem formulation

In the IGPN analyzed in this study, as shown in Fig. 1, the PN has five buses with two power plants where power plant G1 utilizes coal (CFPP) with fixed coal price and power plant G2 operates with NG (GFPP) fed from g15 of the GN [2]. Table 1(a), Table 1(b), Table 1(c), Table 1(d), Table 1(e), Table 1(f), Table 1(g)(a)-(g) show IGPN parameters specifications including the allowed range for production of NG resources and NG loads, GN nodes pressures, GL physical characteristics, gas turbine

Optimality conditions

The energy pricing optimization formulation of IGPN described by Eq. (25) can be written as a general nonlinear programming problem, $\min f (x)$ subject to: $\begin{array}{l} g (x) = 0 \\ h_{l} \leq h (x) \leq h_{u} \\ x_{l} \leq x \leq x_{u} \end{array}$ where $f (x)$ is a general notation for objective function and $g (x) and h (x)$ are sets of equality and inequality constraints, respectively. The lower and upper limits of inequality constraints are $h_{l}, and h_{u}$ and lower and upper limits for variables are $x_{l}, and x_{u}$ , respectively. The implementation of IPA encompasses four steps,

Scenarios

For the purposes of studying the effects of techno-economic constraints and examination of interdependencies of optimal GLMP and ELMP, several scenarios are introduced. Scenario NORM is defined to reveal the results of optimization in IGPN under normal operation and, to serve as the basis for comparison of the obtained results with those of other scenarios. The motivation for definition of RPF, RGF, and RGPF is to simulate the conditions in which a typical GL or PL are not able to carry more NG

Results and discussion

The simulation results for optimal energy pricing of the IGPN analyzed in this study based on the scenarios described above are discussed in this section. It is emphasized that, in this study, the interdependency of energy prices is observed and GLMP (at GN nodes) and ELMP (at PN buses) are determined simultaneously, based on TEC minimization for the IGPN. As shown in Table 1(a), Table 1(f)(a) and 1(f), with the exception of NG load for GFPP, other NG and electrical loads are constant. To

Conclusions and recommendations

In this study, the optimal locational marginal prices for an IGPN are examined based on IPA for optimization, where various techno-economics constraints are accounted for. The effects of techno-economic constraints considered are analyzed based on several scenarios and, it is found that the results of optimal pricing in this study are highly dependent on IGPN topology examined. Hence, the following conclusions can be drawn.

(a)
The variation of GLMPs is not affected by PN conditions dramatically,

Acknowledgements

The assistance received from A. Sadri at Energy Systems Laboratory during the preparation of the manuscript is appreciated.

Nomenclature

Acronyms

CFPP: coal-fired power plant
CHP: combined heat and power
DIS: influence of distance between GFPP and natural gas network (scenario)
ED: Economic Dispatch
EIA: Energy Information Administration
ELMP: electricity locational marginal price
GEOF: natural gas and electricity optimal power flow
GFPP: gas-fired power plant
GL: natural gas pipeline
GLMP: natural gas locational marginal price
GN: natural gas network
GTC: gas turbine compressor
IGPN: integrated power and natural gas network
IMP: GFPP improvement efficiency (scenario)
IPA

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Optimal energy pricing for integrated natural gas and electric power network with considerations for techno-economic constraints

Highlights

Abstract

Introduction

Section snippets

Literature review

IGPN modeling and problem formulation

Optimality conditions

Scenarios

Results and discussion

Conclusions and recommendations

Acknowledgements

Nomenclature

Acronyms

Energy

Electr Power Syst Res

J Nat Gas Sci Eng

Electr Power Syst Res

Energy

Appl Energy

Energy Procedia

Electr Power Energy Syst

Natural gas and electricity optimal power flow

Proc 2003 IEEE/PES Transm distribution Conf Expo

Spot pricing of electricity

Interactions between gas and electricity markets

IEEE; Power Eng Rev

A modeling and optimization approach for multiple energy carrier power flow

IEEE Trans Power Syst