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We model the economic incentives for market participants to cooperate in the development of a microgrid in a small electricity network served by a regulated utility. The microgrid can provide energy, ancillary services, heat and enhanced reliability to its customers. Using the framework of cooperative game theory, assuming exchangeable utility and full public information, we quantify how microgrid development affects prices, costs and benefits for parties in the network under alternative sets of assumptions. Our analysis yields three main results. First, market failures mean that the misalignment between the social objective (market efficiency) and objectives of private parties (profit maximization and consumer surplus maximization) can incent investment in inefficient scale and types of microgrid installations. We discuss how regulators can facilitate the realization of efficient market outcomes. Second, if the regulator does not correctly anticipate the timing of microgrid introduction and does not account for it in the ratemaking process, social benefits associated with microgrid development could be lower and suboptimal investments may take place, relative to the case in which there is no regulatory lag. Third, utility customers could reap most of the benefits of microgrid introduction, if microgrids result in lower electric rates for all customers. However, when regulated prices are above the marginal cost of power provided by the utility, introducing microgrids can instead raise rates and the resulting economic losses to utility customers might exceed the economic gains to the microgrid owner and its consumers. Our study shows how the cooperative game framework can be useful to regulators and policy makers for identifying the beneficiaries of microgrid promotion policies, and for correcting the market failures in utility pricing that can distort incentives for microgrid investment.

This paper describes the upper level of a two-tiered sustainability assessment framework (SAF) for determining the optimal synthesis/design and operation of a power network and its associated energy production and storage technologies. The upper-level framework is described, and results for its application to a test bed scenario given by the Northwest European electricity power network presented. A brief description of the lower level of the SAF is given as well. In order to analyze the impact of microgrids (MGs) in the main network, two different scenarios are considered in the analysis, i.e., a network without MGs and a network with MGs. The optimization is carried out in a multi-objective, quasi-stationary manner with producer partial-load behavior taken into account via nonlinear functions for efficiency, cost, and emissions that depend on the electricity generated by each nonrenewable or renewable producer technology. Results indicate for the particular problem posed and for the optimal configurations found that including MGs improves the network relative to reductions in capital and operating costs and to increases in network resiliency. On the other hand, total daily SO2 emissions and network exergetic efficiency are not improved for the case when MGs are included.

In this paper, multiobjective optimization is proposed for evaluating the sustainable synthesis/design and operation of sets of small renewable and non-renewable energy production technologies coupled to power production/transmission/distribution networks via microgrids. The optimization is conducted over a quasi-stationary twenty four hour, winter period. Partial load behavior of the generators is included by introducing non-linear functions for efficiency, costs and emissions as a function of the electricity generated by each technology. A new index for resiliency is included in the multiobjective optimization model in order to account for the capacity of the power network system to self-recover to a new normal state after experiencing an unanticipated catastrophic event. Since sustainability/resiliency indices are typically not expressed in the same units, fuzzy logic and an explicit set of weighting factor methods are employed to calculate a composite sustainability-resiliency index. Results indicate for the particular problem posed that the inclusion of microgrids into the network leads to a better overall network efficiency, a reduction in life cycle costs, and an improved network resiliency. On the other hand, total life SO2 emissions and network reliability are not improved for this particular case when microgrids are included.

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