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Renewable energy-based generation facilities emerging in microgrids are modifying many traditional principles of economic dispatch because of the variability and uncertainty of their output characteristics. Since the power generation from renewable resources is difficult to anticipate, a real-time adjustment of generation schedules is necessary after more forecast information becomes available. Intra-hour, or ultra-short-term dispatch, allows microgrid operators to frequently update generators’ outputs for providing power supplies efficiently in an uncertain operating condition. However, several technical challenges such as information availability, control architecture, and computational burden make it difficult to find an optimal generation schedule at runtime. This work addresses the intra-hour economic dispatch problem by designing a closed-loop distributed model predictive control (DMPC) that reduces potential variations in the determined generation schedules. At first, optimization problems regarding the behaviors of several participants in a microgrid are formulated. Then, the procedures for achieving the real-time power balance under ramp-rate constraints are detailed. Simulation results present the efficiency of the proposed DMPC for the optimal intra-hour economic dispatch when multiple types of generation resources are considered in a microgrid.

A hierarchical approach for the energy management of geographically close microgrids connected through a dedicated AC power network is proposed in this paper. The proposed approach consists of a two-layer energy management system (EMS) for networked microgrids. In the lower layer, each microgrid solves its own economic dispatch problem through a distributed model predictive control approach that respects capacity limits and ramp-rate constraints of distributed generation. In the upper layer, the energy trading in the network of microgrids decides how to optimally trade the energy based on the marginal cost information from the lower layer in order to improve global optimization objectives, e.g., social welfare. In order to solve the trading problem, a consensus-based algorithm and a replicator dynamics algorithm are proposed assuming that the marginal cost function of the microgrid is known and linear. It is shown that both algorithms converge to the same solution, which is equivalent to the minimization of operation costs. The consensus-based algorithm is extended in order to tackle more general marginal cost functions and trading network constraints. Moreover, the effect of ramp constraints and network limits is studied. Simulations show the effectiveness of the proposed algorithms for three interconnected microgrids with different characteristics.

Power system operation has been a very complex and interesting problem, and currently has gathered more attention because of new elements in the network. New elements such as renewable sources, demand response, electric vehicles, and energy storage systems, are crucial because of their stochastic behavior. Stochastic variables present new challenges for the system operator, who must prevent future changes in the network status by taking efficient decisions with available information and guarantee the system security. In this document we show that Model Predictive Control (MPC) might be appropriate to solve the economic dispatch problem when feasible future events are considered. A case study is solved from different perspectives of the system operator, which is able to consider or neglect future events. Results show that MPC efficiently solves the economic dispatch problem when considering future events and ramp rate constraints.

Planning of protection systems plays a significant role to achieve an efficient behavior (or performance) of distribution systems. Feeders that offer high robustness and reliability are essential to ensure continuous energy delivering to all customer loads. Since distributed generation (DG) has increased its penetration in distribution systems, it has become necessary to place protective devices (such as reclosers) in order to allow DG islanded operation, which decreases the energy not supplied. This paper addresses efficient placement of protective switches, which mainly comprises circuit breakers and reclosers. Two optimization approaches are proposed to enhance reliability indices. First, a single objective optimization problem is defined to maximize the profit of the distribution system operator. Such problem is solved through implementing the differential evolution algorithm (DE). Second, a multi-objective optimization problem is defined to reduce the system average interruption duration index (SAIDI), the system average interruption frequency index (SAIFI), and system costs. This approach yields optimal planning of switches by applying the non-dominated sorting differential evolution algorithm (NSDE). The heuristic algorithms are implemented in Matlab, while the power system is modeled in DIgSILENT in order to use a co-simulation environment between these two tools. Simulations are developed on a real test feeder for two cases: (i) without DG; and (ii) with 6 MW penetration of distributed sources. Results show the importance of both DG and protective devices to enhance reliability, and the effect of enabling intentional islanded operation.

Operation planning models for hydro-dominated power systems usually use low temporal resolutions due to the excessive computational burden, thus ignoring short-term characteristics of such systems. As a result, in systems coupled with wind energy, such models may fail to accurately capture wind variability, and may not appropriately take into account potential consequences of uncertainty on the system operation. This paper addresses this drawback by (i) “controlling” the cost associated with the operation of a hydro-dominated power system equipped with wind power and batteries via a risk-measure and (ii) formulating the short-term load balance as probabilistic constraints in order to hedge against potential extreme wind power scenarios. The risk-averse scheme is embedded in the stochastic dual dynamic programming framework. Simulation results for a case study on a real industrial setting show that hedging the system against the short-term volatility of wind power contributes to mitigating the risk of excessive operations costs or load curtailments, and that the consideration of the decision maker risk profile contributes to decreasing the variability of the solutions. In addition, the results of the application also illustrate the potential of the scheme to assess the energy situation of a country or a region under the penetration of wind energy and batteries deployment. © 2019 Elsevier Ltd

This paper applies the conceptual framework applied in the work of Oum et al. to hedge retailers against price-quantity fluctuations in spot electricity markets, and extends it to power generators, in order to design suitable power options with optimal strike prices from a market maker's perspective. These options are then used to hedge agents against price and quantity fluctuations by maximizing a static expected utility problem. An infinite collection of derivatives (“exotic option”) emerges as the solution of both price and quantity hedging. This exotic option is approximated with a portfolio composed by bonds, forward/futures contracts, and a fixed number of put and call options, employing a plausible replicating strategy. The theoretical framework is tested within the context of the Colombian power market, and is applied to month-ahead and quarterly-ahead hedging during on-peak hours. The proposal addresses major problems such as lack of liquidity and anonymity of the current bilateral electricity trading scheme in Colombia.

Mediante una metodología basada en un modelo de optimización multiperiodo de mínimo costo y un modelo para la estimación de demandas de movilidad, se evalúan diferentes escenarios de movilidad y políticas regulatorias de carácter energético y ambiental para el transporte urbano del Área Metropolitana del Valle de Aburrá. Se espera que la metodología y herramientas desarrolladas apoyen y orienten el proceso de toma de decisiones sobre el transporte urbano local hacia la disminución de la emisión de contaminantes a la atmósfera. La metodología considera alta desagregación geográfica de la información para apoyar el proceso de planificación del territorio./ Through a methodology based on a minimum cost multi-period optimization model and a previous model for mobility demand estimation, there are assessed different mobility demand scenarios next to energy and environmental regulatory policies for the urban transportation of the Aburrá Valley. It is expected the developed methodology and tools to support and guide decision processes over urban transportation sector at a local scale pointing to diminish pollutant emissions to atmosphere. This methodology considers high geographic dissagregation level of the information to support territorial urban planning.

This paper presents the approach undertaken, and the four energy scenarios that have been developed, to support long term energy policy in Colombia. The scenarios were constructed with emphasis on maximum interaction between stakeholders in the Colombian energy sector. The process directly involved over 120 people. The scenarios were developed as strategic support tools for the Energy and Mining Planning Unit (UPME), which is the Colombian institution in charge of developing the country's energy strategies and National Energy Policy. The methodology employed is presented, followed by a detailed description of each of the four scenarios.