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The large-scale deployment of distributed energy resources will produce reverse power flows, voltage, and congestion problems in the distribution networks. This paper proposes a novel optimization model to support distribution system operators planning future medium voltage distribution networks characterized by high penetration of behind-the-meter distributed energy resources. The optimization model defines the optimal mix, placement, and size of on-load tap charger transformers and energy storage devices with the objectives of mitigating network technical problems and minimizing both investment and operation costs. The proposed optimization model relaxes the non-convex formulation of the optimal power flow to a constrained second-order cone programming model and exactly linearizes the non-linear model of the on-load tap changer transformer via binary expansion scheme and big-M method. These two transformations reduce the computational burden of the optimization allowing it to be applicable to real-scale distribution grids, as demonstrated by the results. The numerical results also show that the joint optimization of energy storage devices and on-load tap changer transformers produces a more affordable and flexible planning strategy than the individual optimization of the technologies.

In developing countries, mini-grids are seen as an important option to improve electrification rates in rural areas. In order to be successful, mini-grids face issues of operation and sizing of generation capacities. Current studies on the optimal sizing of mini-grids do not include capacity expansion feedbacks regarding the operator's or investor's long-term economic performance on growth in electricity usage, e.g. gap between demand and supply impacting the operator's income. Using a System Dynamics model, this paper compares the impact from two capacity expansion strategies on rural mini-grid operator's long-term economic performance. The two capacity expansion strategies are: a strategy with minimized costs and a strategy where only diesel power is allowed. Research shows that a cost-minimized capacity expansion strategy might not be the most beneficial solution for the operator's long term financial performance. Specifically, the high investment costs prohibit the implementation of the cost-minimized expansion strategy. In addition, the diesel-only expansion strategy suffers from high operational costs, which creates long-term challenges as the share of diesel increase. Therefore, the timeline of the investments and when to implement different strategies is important, creating a benefit for a System Dynamics approach.

Nowadays, a significant number of distribution systems are facing problems to accommodate more photovoltaic (PV) capacity, namely due to the overvoltages during the daylight periods. This has an impact on the private investments in distributed energy resources (DER), since it occurs exactly when the PV prices are becoming attractive, and the opportunity to an energy transition based on solar technologies is being wasted. In particular, this limitation of the networks is a barrier for larger consumers, such as commercial and public buildings, aiming at investing in PV capacity and start operating as microgrids connected to the MV network. To address this challenge, this paper presents a coordinated approach to the microgrid investment and planning problem, where the system operator and the microgrid owner collaborate to improve the voltage control capabilities of the distribution network, increasing the PV potential. The results prove that this collaboration has the benefit of increasing the value of the microgrid investments while improving the quality of service of the system and it should be considered in the future regulatory framework.

Optimal microgrid design is a challenging problem, especially for multi-energy microgrids with electricity, heating, and cooling loads as well as sources, and multiple energy carriers. To address this problem, this paper presents an optimization model formulated as a mixed-integer linear program, which determines the optimal technology portfolio, the optimal technology placement, and the associated optimal dispatch, in a microgrid with multiple energy types. The developed model uses a multi-node modeling approach (as opposed to an aggregate single-node approach) that includes electrical power flow and heat flow equations, and hence, offers the ability to perform optimal siting considering physical and operational constraints of electrical and heating/cooling networks. The new model is founded on the existing optimization model DER-CAM, a state-of-the-art decision support tool for microgrid planning and design. The results of a case study that compares single-node vs. multi-node optimal design for an example microgrid show the importance of multi-node modeling. It has been shown that single-node approaches are not only incapable of optimal DER placement, but may also result in sub-optimal DER portfolio, as well as underestimation of investment costs.