A Novel Hybrid Framework for Co-Optimization of Power and Natural Gas Networks Integrated With Emerging Technologies

Mirzaei, Mohammad Amin; Nazari-Heris, Morteza; Mohammadi‐Ivatloo, Behnam; Zare, Kazem; Marzband, Mousa; Anvari‐Moghaddam, Amjad

doi:10.1109/jsyst.2020.2975090

Cited by 65 publications

(44 citation statements)

References 37 publications

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“…Various optimization problems, including linear/nonlinear problems, can be solved using GAMS. The presented scheme for the air conditioning system and TESS in this study is a nonlinear model, which is handled by GAMS software applying CONOPT solver [13,14]. CONOPT is a known GAMS solver for dealing with complex nonlinear problems, which has a high rate of efficiency and reliability for a vast class of optimization problems.…”

Section: Materials and Methodologymentioning

confidence: 99%

Scheduling of Air Conditioning and Thermal Energy Storage Systems Considering Demand Response Programs

et al. 2020

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The high penetration rate of renewable energy sources (RESs) in smart energy systems has both threat and opportunity consequences. On the positive side, it is inevitable that RESs are beneficial with respect to conventional energy resources from the environmental aspects. On the negative side, the RESs are a great source of uncertainty, which will make challenges for the system operators to cope with. To tackle the issues of the negative side, there are several methods to deal with intermittent RESs, such as electrical and thermal energy storage systems (TESSs). In fact, pairing RESs to electrical energy storage systems (ESSs) has favorable economic opportunities for the facility owners and power grid operators (PGO), simultaneously. Moreover, the application of demand-side management approaches, such as demand response programs (DRPs) on flexible loads, specifically thermal loads, is an effective solution through the system operation. To this end, in this work, an air conditioning system (A/C system) with a TESS has been studied as a way of volatility compensation of the wind farm forecast-errors (WFFEs). Additionally, the WFFEs are investigated from multiple visions to assist the dispatch of the storage facilities. The operation design is presented for the A/C systems in both day-ahead and real-time operations based on the specifications of WFFEs. Analyzing the output results, the main aims of the work, in terms of applying DRPs and make-up of WFFEs to the scheduling of A/C system and TESS, will be evaluated. The dispatched cooling and base loads show the superiority of the proposed method, which has a smoother curve compared to the original curve. Further, the WFFEs application has proved and demonstrated a way better function than the other uncertainty management techniques by committing and compensating the forecast errors of cooling loads.

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Section: Materials and Methodologymentioning

confidence: 99%

Scheduling of Air Conditioning and Thermal Energy Storage Systems Considering Demand Response Programs

et al. 2020

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“…In [22], a hybrid stochastic/interval/information gap decision theory (IGDT) framework was developed to evaluate the optimal operation of the integrated energy hub system incorporated with the DR concept. A novel hybrid IGDT/stochastic cooptimization strategy for coordinated power and gas grids in the presence of electrical and gas demands, as well as wind energy uncertainties, was developed by [23]. In [24], a multi-energy microgrid operation incorporated with high penetration of RES was optimized via a hybrid stochastic/interval framework exposed by multi-energy demands and RES power output variation.…”

Section: B Literature Reviewmentioning

confidence: 99%

“… In [14][15][16][17][18][19][20], although the authors have investigated the mobility of battery-based energy storage into networkconstrained unit commitment, they have not extensively focused on environmental issues, the flexibility of demand-side resources, and marketclearing process.  In [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], the authors mainly have utilized deterministic, stochastic and robust-based optimization approaches to solve the problem, while the operator at times preferred to differentiate between the risk levels of the existing uncertainties and manage them depending on the different optimization techniques  In [21][22][23][24][25][26], the authors have not applied a hybrid optimization approach in the market-clearing process, while these kinds of techniques can provide major benefits for the market operator to handle uncertainties in real-time dispatch. Hence, this paper applies a new two-stage robuststochastic framework into energy market-clearing constrained to the power grid, environmental issues, and rail transport network (RTN) to achieve high-efficiency scheduling of ESS and handle the uncertainties associated with demand and wind power generation.…”

Section: Contributionsmentioning

confidence: 99%

“…The constraints related to the BEST system in the first stage are defined as 14- (23). The limitation related to the location state of the BEST system is determined by (14).…”

Section: ) First Stage Constraintsmentioning

confidence: 99%

“…The capacity limit of the BEST system is defined as (22). The initial and final state of charge of the BEST system is limited to (23)…”

Section: ) First Stage Constraintsmentioning

confidence: 99%

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Two-Stage Robust-Stochastic Electricity Market Clearing Considering Mobile Energy Storage in Rail Transportation

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This paper proposes a two-stage robust-stochastic framework to evaluate the effect of the battery-based energy storage transport (BEST) system in a day-ahead market-clearing model. The model integrates the energy market-clearing process with a train routing problem, where a time-space network is used to describe the limitations of the rail transport network (RTN). Likewise, a price-sensitive shiftable (PSS) demand bidding approach is applied to increase the flexibility of the power grid operation and reduce carbon emissions in the system. The main objective of the proposed model is to determine the optimal hourly location, charge/discharge scheduling of the BEST system, power dispatch of thermal units, flexible loads scheduling as well as finding the locational marginal price (LMP) considering the daily carbon emission limit of thermal units. The proposed two-stage framework allows the market operator to differentiate between the risk level of all existing uncertainties and achieve a more flexible decision-making model. The operator can modify the conservatism degree of the market-clearing using a non-probabilistic method based on info-gap decision theory (IGDT), to reduce the effect of wind power fluctuations in realtime. In contrast, a risk-neutral-based stochastic technique is used to meet power demand uncertainty. The results of the proposed mixed-integer linear programming (MILP) problem, confirm the potential of BEST and PSS demand in decreasing the LMP, line congestion, carbon emission, and daily operation cost.

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Introduction and Literature Review of Cost-Saving Characteristics of Multi-carrier Energy Networks

Ardashir

Ghadim

2021

Planning and Operation of Multi-Carrier Energy Networks

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A Novel Hybrid Framework for Co-Optimization of Power and Natural Gas Networks Integrated With Emerging Technologies

Cited by 65 publications

References 37 publications

Scheduling of Air Conditioning and Thermal Energy Storage Systems Considering Demand Response Programs

Scheduling of Air Conditioning and Thermal Energy Storage Systems Considering Demand Response Programs

Two-Stage Robust-Stochastic Electricity Market Clearing Considering Mobile Energy Storage in Rail Transportation

Introduction and Literature Review of Cost-Saving Characteristics of Multi-carrier Energy Networks

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