Journal of Energy Engineering

The amount of small-scaled renewable energy sources is anticipated to increase on the low-voltage distribution networks for the improvement of energy security and reduction of greenhouse gas emission. Malaysia has a high solar irradiance level that is ideal for photovoltaic (PV) systems. However, the growth of the PV systems on the low-voltage distribution networks can create a number of technical issues such as voltage rise, voltage unbalance, and reversed power flow. Usually, these issues happen with little fluctuations. However, these issues tend to fluctuate very rapidly in Malaysia because this region has a very low clear sky index. A large amount of clouds often passes over the country, hence making the incident solar irradiance to be highly scattered and fluctuating. Therefore, the power output of the PV systems fluctuates substantially. To ensure an effective operation of the distribution networks with the PV systems, a novel fuzzy control method is developed and implemented to govern the operation of an energy storage system consisting of a bidirectional inverter coupled with lead–acid batteries. The fuzzy controlled energy storage system is able to mitigate the fluctuating voltage rises and voltage unbalances on the networks by actively manipulating the flow of real power between the networks and the batteries. To verify the effectiveness of this fuzzy control method, an experimental network integrated with 7.2-kW photovoltaic systems was set up. A number of case studies are performed to show that the proposed fuzzy control system is able to mitigate fluctuating voltage rises and voltage unbalances under the highly intermittent power output of the PV systems.

It is well known that the generation resource uncertainty induced by significant wind capacity raises concerns about grid security, price stability, and revenue adequacy. One of the most promising solutions is the use of utility-scale energy storage, although the question of general implementation of this strategy remains unanswered. This paper uses a simplified model to show that simple rules exist that govern the decision to generate or store energy from a hybrid wind-storage system. The heuristics developed consider the combination of storage efficiency, electricity price, and shortfall penalty and wind forecast characteristics to guide the decision of whether to bid energy into the electricity market or not. Specifically, this paper develops the optimal strategy for use of a simplified system of an energy storage unit with a wind generator. The solution is analyzed using a dynamic programming formulation in a simplified framework over a multiperiod planning horizon. The analysis of the solution under all regimes yields insightful structural solutions regarding the conditions under which the wind generator should bid into the energy market and when it should not. The results also provide insight into the specific implications of forecast accuracy and market design on the need for storage. This analysis allows additional conclusions to be drawn about the value of various storage technologies based on their capacity and efficiency characteristics. However, the most important contribution of this work is the understanding of the importance of market penalties in encouraging participants to either improve forecasting ability or, perhaps more realistically, contract storage to mitigate shortfall risk. Improving both forecasting accuracy and storage capabilities results in value reduction for both.

The standard optimal power flow (OPF) problem is an economic dispatch (ED) problem combined with transmission constraints, which are based on a static topology. Prior research has demonstrated that cooptimization of generation dispatch and transmission topology results in cost savings. However, the computational complexity associated with topology control (TC) has been a major deterrent to its implementation. The proposed work investigates the application of a heuristic, a greedy algorithm, to improve the computational time for the TC problem while still maintaining the ability to find quality solutions. An expression is derived, which indicates the impact on the objective for a marginal change in the state of a transmission line. This expression is used to generate a priority list with potential candidate lines for switching, which may provide huge improvements to the system. The advantage of this method is that it is a fast heuristic as compared to using mixed integer programming (MIP) to find a solution. The heuristic is tested on real-time applications such as N-1 events, $N-m$ events ($m$ simultaneous contingencies), and cascading events. The proposed algorithm specifically targets cases that lead to load shedding in the system and provides a quick and efficient method to restore the loads by improving the deliverability of reserves via TC. Simulation results on the IEEE 73 bus test system and the IEEE 118 bus test system suggest a significant improvement in the amount of load served to the system with TC as opposed to without TC with minimal time and computation.

Progressive hedging is a scenario-based decomposition method that can be applied to solve the stochastic unit commitment problem. Progressive hedging is not guaranteed to converge to the global optimal solution since unit commitment involves integer variables. Slow convergence rates and cyclic behaviors have been previously observed in practice. Hedging is conventionally performed on the unit commitment status variables. In this paper, several hedging methods are proposed to improve progressive hedging for the N-1 (single contingency) stochastic unit commitment problem. In particular, hedging on the start-up and shutdown variables is proposed and tested in this paper. The performance of the progressive hedging is rather sensitive to the choice in the penalty factors. Thus, several strategies for choosing the penalty factors are evaluated for the cases when using the unit commitment status variables versus using the start-up and shutdown binary variables as the hedging mechanisms. Finally, a hybrid approach using both progressive hedging and an extensive form stochastic programming formulation is implemented in order to obtain a set of feasible unit commitment solutions and compare different hedging methods.

With high penetrations of variable generation from wind turbines in remote locations, transmission capacity may be inadequate to transfer this relatively inexpensive source of generation to demand centers. The major reason is that transmission corridors into load centers are often congested when the system load is high, and additional wind generation is effectively shut out. In contrast, when the system load is low and the wind is blowing, wind generation may be able to meet most of the load throughout the network subject to the specific limitations of the network’s topology. This paper compares the system costs of two very different ways of reducing congestion on the network to increase the annual amount of potential wind capacity dispatched. The first way uses the standard supply side solution of upgrading transmission capacity on the network. The second way uses a demand-side approach in which deferrable demand shifts the system load from on-peak periods to off-peak periods. In addition, the deferrable demand can be used to offset the inherent variability of wind generation and reduce the amount of reserve generating capacity needed to maintain operating reliability. In fact, reducing the total amount of conventional generating capacity needed to maintain system adequacy for a given amount of installed wind capacity is a major source of cost savings with deferrable demand. The simulation is based on a multiperiod (24 h), stochastic, security constrained optimal power flow (SCOPF) and a reduction of the Northeastern Power Coordinating Council (NPCC) network. This framework includes stochastic forecasts of potential wind generation at multiple sites as inputs as well as deferrable demand (e.g., thermal storage) at different load centers. It determines the optimum patterns of dispatch, reserves, and ramping to maintain reliability over a set of credible contingencies. The results demonstrate that deferrable demand can effectively (1) lower the average wholesale prices for energy;, (2) reduce the installed generating capacity needed to maintain system adequacy; and (3) mitigate the ramping costs associated with wind variability. With a sufficient amount of deferrable demand, the typical daily pattern of load can be flattened, and all the variability of wind generation can be mitigated. The overall conclusion is that deferrable demand reduces the total annual cost of the conventional system substantially more than upgrading transmission capacity, and it is an effective alternative to the standard supply side solution.

This paper presents a novel method for evaluating the performance of conservation voltage optimization (CVO). The method investigates the load-to-voltage (LTV) sensitivity. A time-varying exponential load model is developed to represent the load’s dependence on voltage and other factors. The model parameters are estimated by applying the recursive least squares (RLS) algorithm. The effects of CVO can be assessed by using the estimated model parameters. The proposed RLS-based algorithm is validated by simulation tests and the Euclidian distance–based comparison method. Field test results also show the accuracy and effectiveness of the presented algorithm. To address the uncertainty and variability of the CVO performance, the Kolmogorov-Smirnov test is used to determine the distribution that represents the CVO effect of each substation. The proposed methodology can assist utilities in selecting target substations to implement voltage optimization.

Demand-side energy management systems consist of communication links between consumer homes and a centralized scheduler, whose main responsibility is to make decisions regarding curtailment, shifting, or modification of individual loads so that the aggregate load profile closely matches a predetermined supply function for a given time horizon. One challenge for aggregators using a centralized energy management system is the consumer’s wariness of exposing usage data to third parties. Thus, the transmission of load requests from individual consumers to the centralized scheduler must support the flexibility of anonymizing usage data from potential eavesdroppers. This paper considers how load request times can be hidden from any third party entity by incorporating a packet scheduler that collects individual load requests within a consumer’s home and reschedules the load request packets by inserting dummy packets to hide the true load request instances. The packet scheduler impacts performance of the aggregate energy management system and the consumer’s quality of service by introducing a delay that helps distort true load request times. Furthermore, it impacts the design of the underlying communication infrastructure in terms of required bandwidth. The paper provides an analytical framework for quantifying the trade-off between the system metrics of privacy, bandwidth, and utility of the centralized scheduler when anonymity is enabled via delay and dummy packet insertions.

Wind and solar power generation differ from conventional power generation because of the variable and uncertain nature of their power output. This can have significant impacts on grid operations. Short-term forecasting of wind and solar power generation is uniquely helpful for planning the balance of supply and demand in the electric power system because it allows for a reduction in the uncertainty associated with their output. As a step toward assessing the simultaneous integration of large amounts of wind and solar power, this article investigates the spatial and temporal correlation between wind and solar power forecast errors. The forecast and actual data analyzed are obtained from one of the world’s largest regional variable generation integration studies to date. Multiple spatial and temporal scales (day ahead, 4 h ahead, and 1 h ahead) of forecast errors for the Western Interconnection in the United States are analyzed. A joint probability distribution of wind and solar power forecast errors is estimated using kernel density estimation. The Pearson’s correlation coefficient and mutual information between wind and solar power forecast errors are also evaluated. The results show that wind and solar power forecast errors are inversely correlated, and the correlation between wind and solar power forecast errors becomes stronger as the geographic size of the analyzed region increases. The absolute value of the correlation coefficient is generally less than 0.1 in the case of small geographic regions, while it is generally between 0.15 and 0.6 in the case of large geographic regions. The forecast errors are less correlated on the day-ahead timescale, which influences economic operations more than reliability, and more correlated on the 4-h-ahead timescale, where reliability is more impacted by the forecasts. It is also found that the correlation between wind and solar power forecast errors in summer (July) is relatively stronger than in winter (January). The inverse correlation implies that in systems with high penetrations of both wind and solar power, reserves that are held to accommodate the variability of wind or solar power can be at least partially shared. In addition, interesting results are found through time and seasonal variation analyses of wind and solar power forecast errors, and these insights may be uniquely useful to operators who maintain the reliability of the electric power system.

This paper examines the effects of renewable portfolio standard (RPS) regulation on regional electricity markets. A regional market with one or two power suppliers with the capacity to generate renewable energy and access the tradable green certificate (TGC) market is considered. A monopoly model and a Nash game duopoly model to study the effects of TGC price and RPS percentage are established. Surprisingly, the analytical results indicate that the green power output decreases when the RPS percentage increases in a regional market. The analytical results also show that when the TGC price increases, the green power output increases and the total profit first decreases and then increases. Under both regional market structures, an optimal RPS percentage to maximize the social welfare exists. The difference between the two models is also compared with a numerical analysis. When the TGC price increases, the electricity outputs change more slowly in the duopoly market than in the monopoly market. The optimal RPS percentage to maximize the social welfare is higher in the duopoly market than in the monopoly market.

The authors consider efficient mechanisms to optimize the power consumption within a home, industrial facility, college campus, or other facility or set of facilities. The system is controlled centrally by an energy management controller (EMC), which determines the timing of the operation of some of the devices within the facilities. The authors introduce an approximate dynamic programming (ADP) algorithm for this problem and show that the ADP outperforms a recent dynamic programming (DP) algorithm. However, even the ADP fails to solve sufficiently quickly when applied to larger instances. Therefore, the authors also propose several heuristic scheduling policies that provide accurate solutions in a fraction of the time required by the ADP. The authors discuss the computational performance of the ADP algorithm and scheduling policies, and insights gained from the models.

Assessing investment decisions in wastewater treatment can be difficult given that operators face large fixed and variable costs as well as large amounts of uncertainty in the amount of wastewater inflow as well as other factors. Decision makers can solve these problems by implementing operations research models that capture environmental engineering and economics aspects of the problem. Perfect decisions require perfect information but decisions makers can use stochastic models to hedge their decisions against an uncertain future. This paper presents a stochastic, multiobjective, mixed-integer optimization model for a wastewater treatment plant (WWTP). The WWTP can convert the solid end product from wastewater into methane to produce revenue and renewable energy credits. Alternatively, the solids can be used as biosolids for land application, agricultural markets, or compressed natural gas transportation markets. This stochastic optimization model explicitly considers probabilistic information, and the expected value of perfect information and the value of the stochastic solution provide important information for decision makers. As such, the model considers many aspects of the Smart Grid such as integration between energy and transportation, electricity generation by atypical prosumers (producer and consumer), and overall system planning to reduce negative environmental externalities, to name a few. It is shown that a significant trade-off exists between operational and investment costs and the associated carbon dioxide emissions. The WWTP could reduce the amount of carbon dioxide emissions but the operational and investment costs would be increased. One of the results determined in this paper is that to reduce 1 t of carbon dioxide equivalent emissions (given average energy consumption levels) requires $36, $173, or $371 per day when the range of carbon dioxide equivalent emissions is (177,202] t ${\mathrm{CO}}_2\mathrm{e}$ for the first portion, (160, 177] for the second portion, and [154,160] for the third portion, respectively. Additional alternatives include investments in other renewable energy sources such as solar or processing waste from outside sources for revenue and other benefits.