Kaveh Dehghanpour

Real-Time Multiobjective Microgrid Power Management Using Distributed Optimization in an Agent-Based Bargaining Framework

In this paper, we propose a multi-objective power management procedure for microgrids (MGs). Through this procedure the power management problem is modeled as a bargaining game among different agents with different sets of objective functions. Nash bargaining solution (NBS) is employed to find the solution of the bargaining game. NBS lies on the Pareto-front of the power management problem. Moreover, it introduces a unique and fair balance among the objective functions of different agents and removes the need to track the whole Pareto-front in real-time. Distributed gradient algorithm is applied to find the NBS through a modular distributed decision framework without using a central control unit. In this way, the problem of data privacy of different parties within the MG is addressed. The proposed methodology has been tested through simulations on islanded and grid-connected MGs under different pricing scenarios (fixed versus time-of-use pricing).

Frequency stabilization of an islanded microgrid using droop control and demand response

Microgrids (MGs) are attracting a significant amount of attention from researchers for their potential to increase grid reliability through the use of energy storage and local generation, making them ideal for incorporating renewable energy sources as well as smart grid applications. Much work has been done on frequency regulation of islanded MGs from generation-side management as well as through demand response (DR). This paper investigates frequency stabilization of islanded MGs through DR and generation-side management, using droop control on each, allowing them to share generation imbalances. This method proves to be effective by reducing transient time, lowering the frequency deviations, and preventing generators from exceeding their power ratings during disturbances. The MG modeled in this system consists of a synchronous generator and two full converter models to demonstrate transient stability in an MG during a sudden loss of renewable generation. Simulation results demonstrate the effectiveness of the proposed approach.

Agent-Based Modeling in Electrical Energy Markets Using Dynamic Bayesian Networks

Due to uncertainties in generation and load, optimal decision making in electrical energy markets is a complicated and challenging task. Participating agents in the market have to estimate optimal bidding strategies based on incomplete public information and private assessment of the future state of the market, to maximize their expected profit at different time scales. In this paper, we present an agent-based model to address the problem of short-term strategic bidding of conventional generation companies (GenCos) in a power pool. Based on the proposed model, each GenCo agent develops a private probabilistic model of the market (using dynamic Bayesian networks), employs an online learning algorithm to train the model (sparse Bayesian learning), and infers the future state of the market to estimate the optimal bidding function. We show that by using this multiagent framework, the agents will be able to predict and adapt to approximate Nash equilibrium of the market through time using local reasoning and incomplete publicly available data. The model is implemented in MATLAB and is tested on four test case systems: two generic systems with 5 and 15 GenCo agents, and two IEEE benchmarks (9-bus and 30-bus systems). Both the day-ahead (DA) and hour-ahead (HA) bidding schemes are implemented. The results show a drop in market power in the 15-agent system compared to 5-agent system, along with a Pareto superior equilibrium in the HA scheme compared to the DA scheme, which corroborates the validity of the proposed decision making model. Also, the simulations show that introduction of an HA decision making stage as an uncertainty containment tool, leads to a more stable and less volatile price signal in the market, which consequently results in flatter and improved profit curves for the GenCos.

Wind power forecasting: Comparing two statistical signal processing algorithms

Wind power forecasting (WPF) has turned into a substantial tool for limiting the negative impact of wind power intermittency on power system. In this paper, we study and compare two different WPF algorithms: classical autoregressive model (AR), as a base case method, and kernel density estimation (KDE) with minimum mean square error estimator (MMSE). Using the data history of a wind farm in Colorado, these two algorithms are implemented in MATLAB and used to produce 24 hours ahead predictions of wind power time series of the said wind farm. The results obtained from the two methods are then compared from various perspectives (precision, applicability, etc.). The comparisons show that although AR-based wind power prediction has slightly less error than KDE, AR-based prediction does not produce probability density function (PDF) of wind speed/power, while KDE does. PDF of wind speed/power is an important parameter for estimating the required reserve allocation in economic dispatch studies.

An Agent-Based Hierarchical Bargaining Framework for Power Management of Multiple Cooperative Microgrids

In this paper, we propose an agent-based hierarchical power management model in a power distribution system composed of several microgrids (MGs). At the lower level of the model, multiple MGs bargain with each other to cooperatively obtain a fair, and Pareto-optimal solution to their power management problem, employing the concept of Nash bargaining solution and using a distributed optimization framework. At the highest level of the model, a distribution system power supplier, e.g., a utility company, interacts with both the cluster of the MGs and the wholesale market. The goal of the utility company is to facilitate power exchange between the regional distribution network consisting of multiple MGs and the wholesale market to achieve its own private goals. The power exchange is controlled through dynamic energy pricing at the distribution level, at the day-ahead and real-time stages. To implement energy pricing at the utility company level, an iterative machine learning mechanism is employed, where the utility company develops a price-sensitivity model of the aggregate response of the MGs to the retail price signal through a learning process. This learned model is then used to perform optimal energy pricing. To verify its applicability, the proposed decision model is tested on a system with multiple MGs, with each MG having different load/generation data.

Primary frequency regulation in islandec microgrids through droop-based generation and demand control

Conventionally, droop control has been used for primary frequency control, allowing generators to share imbalances in generation and load. This paper proposes the use of different types of droop-based control logics for load regulation to turn on or off groups of loads to help restore the system power balance. The proposed droop-based controllers are used by an aggregator to identify individual loads that can be used for demand response (DR) and control them according to their assigned priority. This procedure incorporates an incentive provided by the utility to the customer to allow control of their loads. Also, we will show that droop control can be used on a variety of resources in an MG at the same time, including energy storage system (ESS), generator, and loads to cooperatively contribute to frequency stabilization. Numerical experiments presented show that the proposed method is an effective way to prevent large frequency deviations due to variations in renewable generation and power contingencies in islanded MGs.