A Metaheuristic Algorithm for Flexible Energy Storage Management in Residential Electricity Distribution Grids
- The conceptualization of the mathematical model for three storage management approaches;
- The adaptation of the general GA structure using common encoding for the three proposed scenarios;
- The validation of the proposed algorithm in a case study that uses a real LV EDN from Romania; and
- Discussions regarding the possible advantages and disadvantages of each storage solution.
2. Related Literature
- In the standard approach, where the individual prosumers acquire storage batteries together with the PV system, and employ them mainly to defer the use of surplus generated during the daytime for the peak load hours, in order to lower their daily costs of electricity.
- In a novel approach, when the storage system is installed in the network at the initiative of the DNO, with the main aim of improving the operation conditions of the EDN. In this case, storage can be seen as
- individual batteries placed in different locations in the network; and
- a single community storage system .
3. Materials and Methods
3.1. The Genetic Algorithm
3.2. The Energy Storage Management Problem
- the LV network is operated in a three-phase, four-wire configuration and supplies one-phase residential consumers;
- the demand pattern is unbalanced in space due to the uneven distribution of the consumers (as number and power demand, as connection on the phases), and unbalanced in time because of the normal demand variation of each consumer;
- the prosumers connected in the network use PV panels for generating electricity, primarily for their own consumption;
- to avoid injecting the prosumer surplus back into the grid, a number of equal capacity storage batteries will be placed in the network; and
- the optimal placement of the batteries is performed so that the energy loses computed in the network for a time interval of 24 h, with the bus loads affected by the charge and discharge of the stored energy, will be minimized.
- The voltage magnitude Uhn must not exceed the allowable upper and lower limits in each bus n = 1, …, NN and in each hour h in the interval of analysis h = 1, …, H:
- The current flow Ihb must be lower than the allowable ampacity (Imax) on all branches from the EDN, b = 1, …, NB and in each hour h in the interval of analysis h = 1, …, H:
- The state of charge (SOC) limits for the storage batteries should not exceed the technical limits for all the batteries s = 1, …, NSS, in each hour h in the interval of analysis h = 1, …, H:
3.3. The Adaptation of the Genetic Algorithm for the Storage Management Problem
- For DNO priority:
- Scenario 1 (all the batteries should be installed at the same bus): All the values from b1 to bNSS must be positive integers and equal, in the range (1, NN) (the total number of buses in the EDN); the values from ph1 to phNSS can have the value 1, 2, or 3, denoting the phases a, b, or c:
- Scenario 2 (the batteries can be installed at different buses and phases): All the values from b1 to bNSS must be positive integers, in the range (1, NN); the values from ph1 to phNSS can have the value 1, 2, or 3, denoting the phases a, b, or c.
- For prosumer priority:
- Scenario 3: (batteries installed at prosumer residences): All the values from b1 to bNSS must be positive integers, denoting prosumer codes (because more than one prosumer can be located at a given bus), for prosumers that have surplus, a small subset of the entire bus range; the values from ph1 to phNSS can be 1, 2 or 3, depending on the phase of connection used by the prosumer PS chosen for storage installation.
- For Scenario 1: the connection buses for the offspring chromosomes, which must be the same for all the batteries, were chosen with random probability from the buses used by the parent chromosomes (as in Figure 3a); and
- For Scenarios 2 and 3, the crossover for buses was applied using the same random mask as for the phases (as in Figure 3b).
- For Scenario 1: a phase gene can be mutated to any value 1, 2, or 3; but if a bus gene is selected from the mutation, then the entire first half of the chromosome is also mutated;
- For Scenario 2: any phase gene can be randomly mutated to any value 1, 2, or 3, and any bus gene can be mutated to any value describing a valid bus number; and
- For Scenario 3: the mutation is first performed on the buses by randomly replacing a prosumer with another from the available pool; then, its corresponding phase is replaced accordingly in the second half of the chromosome.
4.1. The Reference Case
4.2. Scenario 1—Batteries Installed at the Same Bus
4.3. Scenario 2—Batteries Can Be Installed at Different Buses and Phases
4.4. Scenario 3—Batteries Can Be Installed Only in Prosumer Buses
Conflicts of Interest
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|Number of buses||121|
|Number of consumers||113|
|Total load (24 h/06:00—18:00)||219.85/76.01 kW|
|Total prosumer generation||122.00 kW|
|Total prosumer surplus||75.38 kW|
|Network type||Overhead, classic|
|Total/main feeder length||4840/2240 m|
|Scenario 3||107 (85)||83 (63)||107 (85)||94 (119)||44 (37)||1||2||1||2||3||7.61|
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Ivanov, O.; Neagu, B.-C.; Grigoras, G.; Scarlatache, F.; Gavrilas, M. A Metaheuristic Algorithm for Flexible Energy Storage Management in Residential Electricity Distribution Grids. Mathematics 2021, 9, 2375. https://doi.org/10.3390/math9192375
Ivanov O, Neagu B-C, Grigoras G, Scarlatache F, Gavrilas M. A Metaheuristic Algorithm for Flexible Energy Storage Management in Residential Electricity Distribution Grids. Mathematics. 2021; 9(19):2375. https://doi.org/10.3390/math9192375Chicago/Turabian Style
Ivanov, Ovidiu, Bogdan-Constantin Neagu, Gheorghe Grigoras, Florina Scarlatache, and Mihai Gavrilas. 2021. "A Metaheuristic Algorithm for Flexible Energy Storage Management in Residential Electricity Distribution Grids" Mathematics 9, no. 19: 2375. https://doi.org/10.3390/math9192375