BeagamK. Sarmila Har1
AMahabuba2
RJayashree1
HameedJennathu Beevi Sahul1
-
(Department of Electrical and Electronics Engineering, B.S. Abdur Rahman Crescent Institute
of Science and Technology, Chennai, India)
-
(Electrical Engineering Faculty Member, Dubai Men’s College Higher Colleges of Technology,
Dubai, U.A.E.)
Copyright © The Institute of Electronics and Information Engineers(IEIE)
Keywords
Participant based distributed slack reactive power flow model, Deregulated power system, Incremental loss factor, Loss allocation, Market center concept, Linear programming (LP)
1. Introduction
Around the world, electric utilities are undergoing restructuring to transition from
a vertically integrated system to a competitive market structure. Markets differ depending
on the particular needs of the nation or place. To guarantee the reliability and security
of the active power supply into the transmission network, reactive power support is
crucial. The primary market sometimes referred to as the active power market, would
continue to be a competitive market [1-3]. The voltage security issue is taken into account in the OPF technique and is integrated
into the cost-based reactive-power pricing strategy [4,5]. A method for allocating reactive electricity costs in unregulated market places.
The reactive power, voltage regulation, and spinning reserve comprise the reactive
power supply system [6,7]. In order to properly allocate reactive power load and losses, the reactive power
delivered through the capacitance of line charging is treated as a distinct source
[8-10].
The data is utilized to compute the generator's and load's contributions for a fair
and appropriate cost distribution [11,12]. The common single-slack bus PF method has a flaw (mismatch), which is that the slack
bus absorbs all losses [13-16]. When the slack bus and power flow method are used, it is seen that the LMP component
for loss varies widely [17-20]. The outcome offers the nominal pricing of the participants along with their ideal
timetables. In this model, the bus voltage violations are also eliminated.
Configuration of the work
• The novelty of the work lies in the introduction and application of the DC Participant
Based Distributed Slack OQF Model for market clearing and settlement in the Reactive
Power Market.
• This model presents a unique approach to efficiently managing and allocating reactive
power resources, addressing challenges related to reactive power flow optimization,
voltage magnitude requirements, and power balance.
• The model's distributed nature and participant-based approach offer a novel perspective
on market clearing and settlement, providing a potentially more effective and fair
solution compared to existing methods.
• By exploring and evaluating this novel model, the study aims to contribute to the
field by presenting innovative ideas and opportunities for future research in the
Reactive Power Mark
2. Recent Research Work: A Brief Review
Numerous studies on improving power quality utilizing various control strategies have
been conducted, and some of them were described below.
Ahmadimanesh and Kalantar, [21] have analyzed the significance of the expansion of renewable energy sources, particularly
solar power-plants, and then they have looked at how these facilities interact with
the market for reactive electricity. Papazoglou et al. [22]have presented a Day-Ahead Local Flexibility Market in Distribution Network by utilizing
a new Successive LP technique for Optimum PF in Distribution Network. Khoshjahan and
Kezunovic [23] have suggested a bidding strategy method for distributed prosumers (DP) aggregator
participation in the real-time market (RTM) for considering flexiramp and energy.
A new pool-based energy market platform created by Sawwas and Chedid [24] was appropriate for developing nations where electric utilities struggle due to a
lack of adequate generation capacity. According to Paladin et al. [25], a micro-energy market based on the widespread use of photovoltaic and battery energy
storage devices might be used. An open-source test system created by Battula et al.
[26] allowed for the dynamic modeling of central controlled whole-sale power markets running
over high-voltage transmission grids. A decomposition-coordination PF method and 2
layers of coordination were created by Dong et al. [27].
2.1. Background of the Research Work
Recent research has shown that employing a distributed slack OQF model for the reactive
power market clearing and settlement is a key contributing element. Using a number
of relaxation strategies, such as SOCP, moment relaxation approaches and Semi-Definite
Programming, Quadratic-Convex (QC) released a convex-relaxation of complete AC optimum
power flow (ACOPF) issue with better relaxational gap. As a result, the literature
failed to take into account the generation units' limited ability to handle reactive-power,
and how it affects the bus voltage and line capacity. Thus, the limitation discussed
in the literature has motivated to do this research work.
The objective of the work is to improve a novel market clearing and settlement structure
for reactive power in an electricity market. The specific objectives include:
• Designing a DC participant-based distributed slack Optimal Power Flow (OQF) model:
• Implementing a market clearing mechanism: Develop a mechanism for efficiently clearing
the reactive power market, taking into account factors such as supply-demand dynamics,
network constraints, and participant preferences.
• Establishing a settlement framework: Design a framework for settling transactions
and payments in the reactive power market.
• Evaluating the performance and effectiveness of the proposed model: Assess the
performance and effectiveness of the developed DC participant-based distributed slack
OQF model and the associated market clearing and settlement framework.
Based on the provided excerpts from the existing literature, here are some potential
gaps or limitations that could be highlighted:
1. Limited focus on reactive power market clearing and settlement: While the mentioned
studies discuss various aspects of energy markets and flexibility, there seems to
be a gap in the literature specifically addressing the market clearing and settlement
mechanisms for reactive power.
2. Lack of consideration for DC participant-based distributed slack OQF model: The
existing literature does not appear to explore the use of a DC participant-based distributed
slack OQF model for reactive power market clearing and settlement.
3. Limite78d analysis of probabilistic configurations and stochastic nature of solar
irradiation: While some studies consider solar power plants and their interaction
with the reactive power market, there may be a gap in addressing the probabilistic
and stochastic aspects of solar irradiation.
4. Insufficient emphasis on the impact of distributed prosumers and smart home energy
trading: The existing literature briefly mentions the participation of distributed
prosumers and the use of micro-energy markets, but there may be a lack of in-depth
analysis on how these factors affect reactive power market operations and settlement.
3. Market Center (A Unique Reference Point)
It is proposed that the Market Center Concept serve as a special point of delivery
or withdrawal for Q assistance. In sections 3.1 and 3.2, it is described and improved
by including the load and generating center. The idea of a market-center is put forth
in order to transparently divide the full transmission loss among DISCO and GENCO
members in pure P-markets by utilizing the average-loss-factor technique [28-30].
3.1. GENCO Participant Incremental Loss Coefficients Referred to Load Center
The GENCO participant's reactive power-injection of balance eqn is expressed below:
The equation for the reactive-power withdrawal balance for DISCO participants is expressed
below:
The reactive power market clearing and settlement using DCParticipant based distributed
slack OQF model is a comprehensive framework designed to optimize and facilitate the
market clearing and settlement process for reactive power in power systems with DC
participants. Here is an overview of the benefits of this model: The model specifically
considers the participation of DC-connected devices, such as converters or generators,
in the reactive power market. By incorporating their characteristics and capabilities,
the model provides a more accurate representation of the reactive power dynamics in
the system. It utilizes a distributed slack optimization approach, which enables efficient
coordination and optimization of reactive power transactions among market participants.
This approach ensures that reactive power resources are optimally allocated while
respecting the constraints and limitations of the power system. It facilitates the
market clearing and settlement process by determining the optimal allocation of reactive
power resources among participants. It considers participant bids, system constraints,
and market rules to ensure fair and efficient market outcomes. The model takes into
account the influence of reactive power transactions on system stability and voltage
control. Optimizing reactive power flows helps maintain voltage levels within acceptable
limits and enhances the overall stability of the power system. Also promotes transparency
and market efficiency by providing participants with accurate and timely information
on market clearing prices, transaction details, and settlement processes. This transparency
encourages fair competition, improves market liquidity, and facilitates efficient
resource allocation. Fig. 1 shows that the Load center (LF of bus generation).
Fig. 1. Load center (LF of bus generation).
The model is designed to handle power systems of varying sizes and complexity. It
can accommodate a large number of participants, diverse system configurations, and
changing market conditions. Its scalability and flexibility make it suitable for different
applications and system scenarios. It incorporates market rules, regulations, and
constraints to ensure compliance with regulatory requirements and market design principles.
It considers factors such as market power mitigation, congestion management, and participant
behavior to maintain a fair and competitive market environment.
The net power-injection in every bus is added to determine the transmission loss,
which is then determined by Eq. (3). This is obtained by subtracting Eq. (2) from Eq. (1), as shown below.
3.2. DISCO Participant Incremental loss coefficients Referred to Generation Center
The incremental transmission loss factor of demand at bus $t$, or INTLd$_t$, is the
amount of additional generation required to meet an increase in demand at bus $t$
when all other demands remain constant and the additional generation is equal to (1
MVAr - loss linked with an increase in 1 MVAr generation at bus $t$). When assessing
INTLdt, the generation center is taken into account. The loss is therefore overestimated
by these incremental loss coefficients [31,32]. Therefore, using the normalisation factor NoFd from the equation, the normalised
coefficients are displayed below in Eq. (4).
Fig. 2 depicts a similar radial-line connecting each bus-demand to the generation-center,
and the length of each line is precisely proportional to the corresponding extra loss-factor.
Fig. 2. Generation center (LF of bus demand).
To address this, we have provided more detailed explanations and to ensure that the
objectives and scope of the study and methodologies are clearly stated in the DC Participant
Based Distributed Slack OQF Model. The model may aim to develop a mechanism for effectively
clearing and settling reactive power transactions in a power system with DC participants.
This involves determining optimal reactive power flows, prices, and allocations to
ensure efficient utilization of reactive power resources. Incorporating the participation
of DC participants in the reactive power market, such as DC generators or converters,
also involves considering their unique characteristics, capabilities, and impact on
system voltages and power flows. It aims to distribute the responsibility of reactive
power provision and consumption among market participants rather than relying on a
centralized entity. This distributed approach allows for flexibility, resilience,
and the potential for innovation in reactive power management. To develop mechanisms
or rules to prevent unfair pricing, manipulation of reactive power flows, or other
market distortions.
3.3. Method for OQF Algorithm
Step 1 Read the maximal count of repetitions (N${}_{max}$), bus-data, mis-match tolerance(
${\rm \varepsilon }_{{\rm Q}} $), line-data, transformer-data, DISCO, GENCO, and
bus-data.
Step 2 To find the optimal timetable [QrG], run a single-sided auction market using
the supply offer curve of aggregation for a lossless system.
Step 3To ascertain the bus voltage magnitude, run the PBDSOQF Method by using the
optimum bus generation schedule [QrG].
Step 4 Determine the GENCO and DISCO loss factors using the bus voltages found in
Step 3 as a starting point.
Step 5 Run the PBDSOQF described in equations (40) to (43) to retrieve the decision variables [QrG] if any of the bus voltages are out of bounds.
Step 6The bus generation schedules obtained in step five should be used to run the
participant-based distributed-slack reactive power flow method.
Step 7 If the bus-voltage is out of compliance Move to step 5; else to 8.
Step 8 It is best to print the bus-voltage and GENCO schedules.
Step 9 Execute market clearing and settlement after obtaining PBPCQrg and PBPQrd from
each bus.
Step 10 Ends
4. Proposed HBA Approach
The metaheuristic optimization method known as the Honey Badger Algorithm depends
on the honey badger's (HBs) clever foraging technique. The development of HBA's exploration
and exploitation stages is inspired by the dynamic search behavior of HB using honey
seeking and digging techniques [33,34]. The following list outlines the HBA procedure in depth:
Step 1 (Initialization):Initializing the count of HBs, which is population positions
and size. In this manuscript, the initialized parameters, such as voltage and current
are initialized by,
where upper and lesser bounds of search domain as$U_{BI} $, $L_{BI} $, random number
implies $R_{1} $, solution in the population of $n$ implies $X_{I} $.
Step 2 (Random generation):The initialized populations are produced at random using
the random generation outlined by
Step 3 (Fitness calculation):The fitness is determined by the objective function and
is defined as
Step 4 (Define intensity):Determine the solution's intensity depending on prey strength
and distance between them. It is defined as follows:
Here, intencity of the prey as $Intensity_{I} $,concentration strength as $s$, distance
amid prey and $i$th badger as$D_{I} $.
Step 5 (Density factor updating):The density factor ($\alpha$) is used to maintain
a seamless transition from exploration to exploitation. As a result, it is characterized
as follows:
where, maximal count of iteration as$t^{MAX} $, constant as$e$ i.e, $e\ge 1$.
Step 6 (Escaping from local optimum):This step is used to avoid the local search,
and flag $F$ is used to avoid the local optimum value.
Step 7 (Updating the agents' positions):To update the location, two steps are used:
digging and honey phase.
Step 7.1 (Digging phase):The updating is executed below,
Step 7.2 (Honey phase):The updating is executed below
Step 8 (Phases of exploitation and exploration):The exploration and exploitation is
executed below
Step 9 (Stopping criteria):Verify the stopping criteria, If the criterion is satisfied,
the ideal result is attained; otherwise, the procedure is repeated [35]. Fig. 3 depicts a flowchart of the HSA technique.
Fig. 3. Flowchart of HBA approach.
The integration of the Honey Badger Algorithm (HBA) is based on the market clearing
and settlement in the Reactive Power Market. Using DC Participant Based Distributed
Slack OQF Model can bring several potential benefits. Here is an overview of how the
HBA can enhance the model: The HBA is known for its ability to handle complex optimization
problems. By incorporating this algorithm into the model, it can improve the optimization
process for market clearing and settlement. The HBA's ability to handle non-linear,
non-convex, and multi-objective optimization problems can enhance the accuracy and
efficiency of the model's reactive power optimization. It is designed to handle uncertainties
and noise in the optimization process. In the context of reactive power market clearing
and settlement, this can be valuable in dealing with uncertainties related to participant
behavior, demand fluctuations, or renewable energy generation. The HBA's robustness
can help ensure reliable and stable market outcomes. This can be advantageous in the
context of reactive power market clearing and settlement, where the number of participants
and the complexity of the power system can be significant. The HBA's scalability can
enable the model to handle larger systems and a larger number of participants effectively.
The HBA is designed to converge quickly to near-optimal solutions. This can reduce
the computational time required for market clearing and settlement, allowing for faster
decision-making and timelier market outcomes.
5. Results and Discussion
Three different systems are used to test the proposed PBDSOQF approach, and the outcomes
are discussed.
1. Radial 5 bus-system
2. Ward and Hale six bus-system
3. IEEE-30 bus-system
5.1. Bus System for Radial 5
On the bus system of radial 5, a PBDSOQF method is investigated. Table 1 provides the supply offer and demand bid for a modified 5 bus, 4 line radial systems.
Base MVA is 100, and every line has a reactance of 0.01 pu. Aggregated offer curves
are used in the proposed market center concept to facilitate market clearing and settlement.
Table 1. Data on supply, demand, and the bus system of radial 5.
|
Data Demand
|
|
DISCO No.
|
Bus
No.
|
Quantity (MVArh)
|
|
1
|
4
|
4
|
|
2
|
5
|
20
|
|
3
|
6
|
180
|
|
Total
|
204
|
|
Supply Offer
|
|
GENCO No
|
Bus No
|
Quantity (MVArh)
|
Price
($ / MVArh)
|
|
1
|
1
|
200
|
20
|
|
2
|
2
|
20
|
15
|
|
3
|
3
|
08
|
12
|
The market for a bus System of Radial five in Single-Sided is depicted in Fig. 4. From the figure, the aggregated supply offer curve is used to operate the auction
market of a single-sided loss-less system, and the best schedules, or QGs, are obtained
Fig. 4. Market for bus system of radial five in single-sided.
Fig. 5 depicts Market Center receives reactive electricity from marginal GENCO one is attached
to bus one and PBPs participants. The optimal schedules QG from Fig. 4 are used to execute the Participant Based Distributed Slack Reactive Power Flow at
0.1MVAr. single-sided auction markets for the loss-less system are operated by utilizing
the 4 supply offer curve, and the best schedule QGs are obtained as shown in Fig. 6. The PBDSOQF is run with a mismatch tolerance of 0.1MVAr by using the optimal schedule
QG found in Fig. 6.
Fig. 5. Market center receives reactive electricity.
Fig. 6. Auction market for the bus system from marginal GENCO.
Running the PBDSOQF with the optimum schedule results in the solution of voltage magnitude
at a mismatch tolerance of 0.1MVAr, is illustrated in Fig. 7. Fig. 8 shows the loss factors discovered for DISCOs and GENCOs by utilizing the ideal schedule.
Fig. 9 shows that the GENCO and DISCO Loss Factors. Fig. 10 depicts the reactive power bus injection prior to and following optimization.
Fig. 7. Auction market for the bus system of IEEE-30 in single-sided.
Fig. 8. Solution of bus voltage.
Fig. 9. GENCO and DISCO loss factors.
Fig. 10. MVAr reactive power bus injection.
Fig. 11. For the Bus System of IEEE-30, participant-based (a) GENCO compensation,
(b) DISCO participant price.
Fig. 11 illustrates that for the bus system of IEEE-30, participant-based compensation is
(a) GENCO compensation (b) DISCO participant price. At the market center, the cost
of compensating for reactive power is 18.163 dollars per MVArh.
Fig. 12. Variations in LMPs for every bus obtained using the proposed.
Fig. 13. Congestion's effects on real power flow and LMPs.
Fig. 14. Congestion effects on (a) reactive power flow, (b) reactive-LMPs.
Fig. 12 displays Variations in LMPs for every bus obtained using the proposed. Figs. 13(a) and 13(b) illustrate the locational marginal pricing of load bus and actual power transferred
from bus 2 to 3 by line 2. Congestion effects on reactive power flow are depicted
in Fig. 14(a). The actual power passing through line two lessens during peak hours to maintain
the constraints of the line in Fig. 14(b). The reactive power flow by line two raises under the peak-hours to keep the voltage
magnitude of load-bus is depicted in Fig. 15. Fig. 16 displays the LMP of load bus over a 24-hour period when various percentages of loads
are shift-able as depicted in Fig. 16(a). Bus LMP is higher for hours 19-21 when 10% of the load is shiftable compared to
20% of the load. Fig. 17 compares the results of the proposed strategic bidding method for three alternative
situations: (1) with enforced ramping limits (ERL), (2) with ERL and shift-able loads
(ERLWSL), (3) non-ERL in Fig. 17(a), as units work at their fullest capacity during these times. However, as illustrated
in Fig. 17(b), the load shifting reduced the negative effects of enforcing ramp limits on LMP.
The effects of reactive power support constraints are shown in Fig. 18.
Fig. 15. Congestion effect on voltage profile of bus three.
Fig. 16. Demand response effect on LMPs. (a) LMP of bus 3 over 24 hours. (b) Load-shifted
over 24 hours.
Fig. 17. Enforcing ramping limitations and the load-shifting effect (a) for bus 1,
the LMP over 24 hours, and (b) for generator 1, the real-power dispatch over 24 hours.
Fig. 18. Effects of reactive power support constraint.
6. Conclusion
The contribution of the study is the construction of a novel Participant Distributed
Slack Reactive Optimal Reactive Power Flow approach for the market clearing and settlement
of Single Sided Auction Market. In this method, the equality requirements are voltage
magnitude at buses, and the inequality constraints are a single power balance equation,
which balances the power supplied by all GENCOs at the market centre and the power
taken by all DISCOs at the market centre. With the help of this model, it determines
each participant's ideal schedule and nodal pricing. In this model, the bus voltage
violations are also eliminated. For quick convergence, the PBDSOQF model is suggested.
The aim of the study ``reactive power market clearing and settlement using DC participant
based distributed slack OQF model'' is to develop and evaluate a novel method for
market clearing and settlement in the Reactive Power Market. The study aims to introduce
the DC Participant Based Distributed Slack OQF model as a solution for efficiently
managing and allocating reactive power resources in the market. The specific objectives
of the study may include optimizing reactive power flow, ensuring voltage magnitude
requirements are met, achieving power balance, and providing fair and equal treatment
to all participants. All participants will be treated fairly and equally by this approach.
On the Radial 5, Ward and Hale six, and IEEE-30 Bus-System, the proposed PBDSOQF model.
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Author
K. Sarmila Har Beagam is working as an Assistant Professor (Sel. Gr) in B.S. Abdur
Rahman Crescent Institute of Science and Technology, Chennai, India.
Mahabuba A is currently working as an Electrical Engineering Faculty Member, Engineering
Technology and Science Division of Dubai Men's College, Higher Colleges of Technology,
Dubai, U.A.E.
Jayashree R is currently currently working as a Professor at B.S. Abdur Rahman Institute
of Science and Technology.
Jennathu Beevi Sahul Hameed
Jennathu Beevi Sahul Hameed is currently working as an Associate Professor in the
Department of EEE, B.S. Abdur Rahman Crescent Institute of Science and Technology,
Chennai. Her areas of interest include deregulated power system, control systems,
soft computing techniques, and electrical machines.