An Optimal Energy Management System for Islanded Microgrids Based on Multiperiod Artificial Bee Colony Combined With Markov Chain
Summary (2 min read)
I. INTRODUCTION
- F EXIBILITY requirements in electric power systems and presence of non-dispatchable intermittent generation leads to development of Microgrids (MG)s [1] .
- Therefore, presenting powerful optimization algorithms to extract the best possible solution for the MGs is very important.
- These techniques are trying to seek good (near-optimal) solutions at a reasonable computational cost without being able to guarantee either feasibility or optimality, or even in many cases to state how close to optimality a particular feasible solution is.
- According to the advantages of this method, it is applied in the present paper for the optimization of MG operation in terms of performance, generation resources scheduling and economic power dispatch.
- The proposed model utilizes ANN for primary predictions.
II. ALGORITHMS IMPLEMENTATION FOR EMS
- It comprises different units, namely ANN-MC, EMS and LEM units.
- As shown in Fig. 1 , four different algorithms are presented for implementing EMS based on LEM by using heuristic techniques or without using any optimization method.
- Flexibility, good accuracy, speed in decision making and plug and play abilities of LEM unit, MCEMS, EMS-MINLP (EMS based on mixed integer non-linear programming) and EMS-PSO (EMS based on particle swarm optimization) algorithms are discussed in detail in the previous studies [1] , [14] , [15] .
- Therefore, these are not addressed in the present paper and only EMS-MABC algorithm is described.
A. EMS-MABC algorithm
- This algorithm encompasses ANN-MC, MABC and LEM units as illustrated in Fig. 1 .
- In the proposed model, two ANNs are used for prediction.
- Then, according to TPM, the probability of predicted value is calculated during the next step.
- The procedure can be summarized as follows (Fig. 3 ): EQUATION Step 1 TPM calculation based on 600 data points of wind speed; Step 2 Design of ANN-1 for primary prediction by using 300 other points of wind speed data; TPM accumulation According to Fig. 3 , ANN-1 designed in the previous step is applied for the primary prediction.
- It must be checked during optimization process if the generated population members satisfy constraints or not.
IV. PROBLEM FORMULATION
- The problem formulation is divided into two parts which are closely connected and dependent on each other.
- The first part is related to the prediction error of uncertainty model and the other include MG constraints.
A. Error criteria for uncertainty consideration
- The prediction error of a model is classically defined as the difference between the measured and predicted values.
- A horizon dependent model error e t t+∆t is given by EQUATION where X denotes non-dispatchable resources and load demand entries.
- The most commonly used evaluation criterion is the MAPE defined as follows [24] .
- EQUATION where ∆t and N describe the prediction horizon and number of prediction, respectively.
- It is very important to reduce MPE because a large prediction error and consequently wrong control commands may cause an unstable condition for non-dispatchable resources.
B. MG mathematical modeling
- The system under study is considered as an islanded MG including non-dispatchable (WT and PV in this study) and dispatchable generation resources (MT in this study) and ES supplying some responsive (EWH and DR in this study)/ non-responsive loads (NRL).
- The objective of economic dispatch problem is in fact minimizing the total production cost while satisfying generation resources constraints.
V. APPLICATION TO TEST GRID
- EMS-MABC algorithm is implemented and validated experimentally over the IREC s MG.
- All the microsources with any characteristic can easily be emulated by digital signal processing.
- This MG has two non-dispatchable resources (PV and WT), a dispatchable resource (MT), and ES integrated with some responsive (EWH and DR) and NRL.
- Emulators specifications are presented in the previous papers [1] .
- Then, all optimal power set-point of each microsource will be dispatched to them at each time interval based on The ability of the proposed algorithm under several scenarios is considered for optimal scheduling and operation of resources, minimizing the generation cost as well as applying demand side management.
VI. RESULTS AND DISCUSSION
- The results of experimental evaluation of the proposed algorithm over IREC s MG are presented.
- During 06:00-12:00 period, MCEMS has already used ES for supplying a part of power shortage, while in EMS-MABC, ES is operated in the charging mode and continuing to reach SOC.
- The DR constraints are expressed with various status flags, the information of other consumers and the excess power generated has been modeled to obtain the minimum total generation cost and less market clearing price.
- This combined programming has been evaluated over a MG Testbed.
- The proposed approach shows more decrease in the objective function than EMS-PSO algorithm while reducing computation time.
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References
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"An Optimal Energy Management System..." refers background in this paper
...Hence, intelligent control systems must be developed to accommodate the ES and the DR in MGs in order to supply consumers as required [6], [13]....
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...An adequate amount of demand-side delivery in MGs has significant importance due to the limitations of using nondispatchable resources [6], [7]....
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610 citations
416 citations
"An Optimal Energy Management System..." refers methods in this paper
...Heuristic algorithms such as a genetic algorithm [16], PSO [17], ant colony optimization [18], and bee colony optimization [19] are some optimization methods used for the UC within MGs [14]....
[...]
407 citations
Additional excerpts
...operation and generation cost [2]–[5]....
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378 citations
"An Optimal Energy Management System..." refers background in this paper
...An adequate amount of demand-side delivery in MGs has significant importance due to the limitations of using nondispatchable resources [6], [7]....
[...]
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Frequently Asked Questions (17)
Q2. What is the importance of reducing MPE?
It is very important to reduce MPE because a large prediction error and consequently wrong control commands may cause an unstable condition for non-dispatchable resources.
Q3. What is the ability of the proposed algorithm under several scenarios?
The ability of the proposed algorithm under several scenarios is considered for optimal scheduling and operation of resources, minimizing the generation cost as well as applying demand side management.
Q4. What is the effect of MC method on the prediction of wind speed?
As the MC method keeps the signalslong-term behavior in the memory, the error obtained from the extrapolated prediction is also reduced.
Q5. how much power is used in EMS-MABC?
by proper selection of MT, ES is operated in the charging mode in EMS-MABC and at the end of this time interval, SOC is about 80%.
Q6. What is the purpose of this paper?
In this paper, to improve exploitation process of classic ABC, a different probability function modifying searching mechanism has been applied to the original ABC algorithm.
Q7. How can dependent variables be generated randomly?
by using valid values for these independent variables and associated constraints, dependent variables can be generated randomly.
Q8. How many neurons are selected in the input layer?
The number of neurons in the input layer is selected by considering the calculation of time and error (maximum prediction error (MPE) and MAPE).
Q9. What are the main modifications of the ABC algorithm?
these modifications are based on reducing the colony size; maintaining the perturbation scheme; and using a rank selection strategy for maintaining diversity.
Q10. What are the main features of the proposed EMS?
good accuracy, speed in decision making and plug and play abilities of LEM unit, MCEMS, EMS-MINLP (EMS based on mixed integer non-linear programming) and EMS-PSO (EMS based on particle swarm optimization) algorithms are discussed in detail in the previous studies [1], [14], [15].
Q11. What is the solution for the economic dispatch?
Update the best solution acquired so farend while Return optimal power set-pointsend forproposed MABC, binary numbers 1 and 0 are used to indicate the status of generating units ON/OFF whereas the economic dispatch is solved using the real coded ABC.
Q12. What is the way to improve strategy throughput?
Improving strategy throughput by constraint based management in MABC whenever the commitment status for each time interval is generated randomly or by the modification of employed/onlooker bee′s position, dispatchable constraint must be checked as follows:Step 1: If dispatchable resources constraints are met, then go to Step 3.
Q13. How many wind speed data is used to improve the model accuracy?
The outline of the proposed model is shown in Fig. 2. A set of wind speed data 2.5s in a 175min period is used to improve the model accuracy for predicting wind speed up to 7.5s ahead (total of 4200 wind speed data).
Q14. What is the MCP value of EMS-MABC?
The minimum value of λMCPt and λ ′MCP t are respectively 0.2 e/kWh and 0.13 e/kWh which are obtained for both algorithms during 00:00-06:00.
Q15. What are the main variables that can be varied depending on the weather?
Since WT and PV are non-dispatchable resources which are affected by weather conditions, MT and ES powers can be varied depending on the power generated by WT and PV and energy consumed by load.
Q16. What is the DR constraint for a MG?
The DR constraints are expressed with various status flags, the information of other consumers and the excess power generated has been modeled to obtain the minimum total generation cost and less market clearing price.
Q17. What are the main types of variables that are considered in this paper?
These variables are divided into two categories of dependent (P i,MTt , P i,ES+ t , P i,ES− t , P i,EWH t and P i,DR t ) and independent (P i,WTt and P i,PV t ) variables.