Coordinated microgrid investment and planning process considering the system operator

Abstract

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. KeywordsDistribution grid voltage control, microgrid design, model predictive control, photovoltaics. IntroductionThe number of photovoltaic (PV) installations have exponentially increased over the last decades and a more accelerated growth, pulled by the emerging economies, is expected by In addition, in order to maximize the on-site DER penetration it is a common practice to apply Demand Response (DR) procedures to decrease the load in peak hour conditions, These technical approaches to improve grid controllability proved to be very efficient in increasing the capabilities of distribution systems to host more PV capacity. However, they entail investment and operational costs to the distribution system operators (DSO) and, especially in scenarios of unbundling electricity sectors, DSOs have no interest in making those investments and bearing those costs. In fact, they have no motivation to enable more PV capacity, since their remuneration depends on electricity consumption and the 6 challenges of solar integration are mostly peak power related [19]. Moreover, the installation of photovoltaic units has the practical effect of reducing the net consumption of the prosumers, which decreases the income of the DSOs.The difficulties of the system to accommodate more solar power persist in most distribution networks, especially those that already have a considerable number of photovoltaic installations connected to the LV network. This is a particular 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 (e.g., [26] and [27]). Three main reasons explain the limitations imposed by the distribution grids to the microgrids investment and planning process: 1) microgrids require a significant amount of PV to justify the investments and, since all the capacity is conc...

Stochastic Planning of Distributed PV Generation

“…Another utility study [4] reveals that reduction in distribution line losses can only be achieved by targeted PV installation at specific locations. Involving the distribution system operator in such planning studies has also been encouraged in [5] to increase utility profits by optimal placement of distributed generators, and in [6] to elevate PV hosting capacity of microgrids while reducing network-related overvoltages. The work in [7] points out that installation of PV systems is motivated by distribution grid efficiency mandates and goes on to explore the financial perspectives of utilities and customers in determining optimal PV locations and sizes.…”

Optimal Operation Analysis of the Distribution Network Comprising a Micro Energy Grid Based on an Improved Grey Wolf Optimization Algorithm

“…According to the research reported in the above references, the control variables of the system will feature an increasing amount of distributed renewable energy in the distribution network, which complicates the solution process, and is disadvantageous to the solution of the model. As a result of the continuous development of microgrid technology [20][21][22][23], experts and scholars in China and abroad studied the optimal dispatching problem of renewable energy in the form of microgrid access to a distribution network [24][25][26][27][28]. For example, Reference [27] considered the various interest demands of the microgrid and the distribution network, established a game relationship model of the two, proposed a cooperative evolutionary game algorithm to solve the above model, and achieved coordinated operation of the microgrid and the distribution network.…”

Stochastic Mixed-Integer Programming (SMIP)-Based Distributed Energy Resource Allocation Method for Virtual Power Plants

“…A two-level gaming approach was made for the optimal demand response management with multiple utilities and users [13]. The reinforcement learning is applied to find optimal power company selection [14].Many studies have been conducted to determine the optimal configuration of DERs in microgrids (MGs) [15][16][17][18][19][20][21]. The DER allocation problem can be formulated by a similar form of optimal sizing and placement problems because both problems involve choosing an optimal combination of DERs to maximize the revenue or minimize the expenses.…”

“…The DER allocation problem can be formulated by a similar form of optimal sizing and placement problems because both problems involve choosing an optimal combination of DERs to maximize the revenue or minimize the expenses. The optimal placement of DERs in the MG are studied deterministically [15][16][17]. However, because the portfolio of a VPP cannot be changed frequently, the VPP operator needs to consider the long-term uncertainty of RESs.…”

“…To this end, stochastic programming has been applied in many studies [18][19][20][21]. Furthermore, systems' constraints (e.g., voltage or flow limit) are often considered because of the sizing and placing problems usually addressed in the MG [17][18][19][20]. However, most studies consider the MG instead of VPPs; thus, there is a difference between determining the optimal investment and optimal portfolio.…”