This chapter brings the thesis to an end, providing the main conclusions and the aspects of interest for future research developments.
6.1
Conclusions
A model based on DSM strategies, as a part of a comprehensive framework including economical and technical issues under the SG concept, has been developed in this thesis. This model makes use of optimisation problems to enable the flattening of the daily electricity load curve, shifting the demand from later time periods to earlier time periods in response to hourly prices. Different types of agents that own distinct elements have been considered. Through the proposed model, they can maximise their benefits or, otherwise, minimise the costs for the energy they need. It has been shown that load shifting can be applied to common grid loads and also to EV charging, thus helping to allocate the demand more efficiently and flattening even more the load curve. This effect depends strongly on the adequate selection of the parameters conditioning the results of the corresponding optimisation problem. In particular, the hourly prices configuration is one the most important factors to take into account since the load will be shifted to those time periods when lower energy prices are expected. In addition, the particular characteristics of the load curve and the elements included in the system have to be also considered. However, the parameter k that defines the maximum number of time periods that load can be shifted, forwards or backwards, has also a significant influence. Regarding this, although the system benefits from a better load rearrangement as the parameter increases, these benefits are not so clear if technical aspects such as losses or power flows are analysed. Results show that an intermediate value for k between 3 and 12 provides more favourable results from both the system’s and agents’ points of view.
With respect to the technical management, a centralised OPF has been developed and tested. Modifying the generators’ output, control variables of the problem, it has been illustrated how the line congestion can be alleviated. However, this approach cannot give a solution in feeders that are overloaded in some of its lines and, additionally, which do not have generators. In the absence of alternative measures, load shedding should be carried out. However, a novel algorithm has been proposed to solve technical congestion problems using the capability of an EV to change its initial expected charging pattern. Using this algorithm, EVs can help the system by charging more than initially required, decreasing or interrupting the charging, or even supplying energy, that is, employing V2G to that end. In this way, EVs can alleviate line congestion regardless of the presence of generators in the feeder. The EVs’ contribution, in terms of active power injection,
6.2 Future work 125 is calculated using DFs and some specific rules to select the most suitable buses with
EVs and how much energy is needed to lead a line to a secure state. For the scenarios presented, it has been demonstrated that a small number of EVs is enough to tackle line congestion although higher levels of congestion require more EVs, also taking into account the increment in reactive power flow due to the changes in bus injection.
An optimisation problem, envisaged to be used by EV aggregators, has been described and studied. This problem allows aggregators to maximise their benefits determining the most favourable time periods for charging and discharging but satisfying the EVs’ mobility requirements at the same time. The influence of different parameters has been highlighted, the EV patterns and the energy prices being the most relevant. It has been shown that EV aggregators need to suitably forecast the electricity prices since they define the time periods in which the charging/discharging should be performed, but it is also necessary to have certain knowledge about the availability of the EVs for connection in those time periods. Results have revealed that EVs have to be charged during night hours whereas the discharging must take place in the latest time periods of the day to allow aggregators to maximise their profits.
Provided that there is a relation between electricity prices and load demand, the EV aggregator strategy leads to a filling of the valleys and s shaving of the peaks. This idea has been highlighted throughout the thesis showing the effect of the EVs on the load curve. Relative to this, a market-clearing procedure taking into consideration techni- cal constraints has been presented including the role of the EV aggregators that bid for charging/discharging along with the conventional elements presented in wholesale mar- kets. Based on the results arising from their optimisation problems, they can bid more efficiently in order to satisfy the EV energy requirements economically, applying V2G to obtain additional benefits. As stated above, this operation allows obtaining a flatter load curve.
6.2
Future work
In this section, some future research developments are suggested.
Regarding DSM strategies, results have been illustrated assuming a time horizon of 24 hours and a particular load curve in all the cases of study. The effect of considering
wider time horizons has not been assessed and it is interesting to analyse how the load curve can be flattened when several days are included. On the one hand, the scope of the model through load shifting can be broadened since SG agents can adapt or improve their decisions against changes in the environment, e.g. daily electricity prices. On the other hand, parameter k can have a clearer effect on the load curve in the intermediate days since the final time period would not restrict the shift of the loads to further time periods.
In relation to the technical tools, the OPF could be extended to make possible tackle any kind of line congestion regardless the generators located at the overloaded feeder. To this end, different control variables have to be defined. Although it is possible to correct voltage limits violations, this issue has not been studied in detail. The joint consideration of these aspects would lead to a more complete formulation of the problem and to a better understanding of its capabilities. Additionally, the algorithm for EV management has proved to be effective if the congestion level is not very high. In other cases, it is necessary to evaluate the change in reactive power flow that indeed influences apparent power and, therefore, the level of congestion of the line, or increase the number of EVs considered to be able to tackle higher levels. Due to the linear nature of the model, arising the DFs formulation, the functionalities of the algorithm should be tested in different scenarios with more EVs and different congestion levels.
Other issues that deserve further research are related to the EV aggregators strategy. As stated in the corresponding chapter, an appropriate forecast of both the electricity prices and EV patterns are needed in order to maximise the benefits of the aggregators. A bad selection of these parameters can lead to undesired results. In practice, valuable information about prices and EV behaviours can be extracted from historical data, e.g. clearing prices in the market or mobility studies. However, new methodologies that allows EV managers to bid more effectively should be studied. A better comprehension and assessment of the risks that aggregators can take in economical terms requires introducing
uncertainty in the optimisation problem through stochastic programming. Moreover,
the proposed strategy allows determination of the most suitable time periods for the EV charging and discharging as well as the required amounts of power. Nonetheless, it does not provide information about how much the aggregators should offer, in wholesale markets, for the energy they need for charging the EVs or the energy they can supply.
6.2 Future work 127 In other words, they have qualitative information about the bidding process. Therefore,
additional research efforts are needed to overcome these drawbacks.
Finally, it has been established that EV aggregators are responsible for satisfying EV mobility requirements and they can also take advantage of V2G capabilities. Thus, they are allowed to buy energy for EV charging and sell energy through EV discharging to obtain additional benefits. However, the provision of other services has not been consid- ered and, therefore, new developments regarding ancillary services such as regulation or reserve can be of interest. Furthermore, considering V2G a reality in the medium/long term, the reduction in the life cycle of the EVs’ batteries due to this mode of operation deserves a deeper research.